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.

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

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

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

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

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
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

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

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

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

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

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
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

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

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

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

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

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

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

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

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
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

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
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

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

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

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

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

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
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

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	23.41 538	85.02 70	99.49 31	91.99 107	98.56 54	98.47 38

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.
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

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

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

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

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

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
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.
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
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

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

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
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

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

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
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

train_agg		93.44 75	93.08 85	94.52 49	97.53 68	86.49 39	94.07 232	96.78 91	81.86 334	92.77 102	96.20 110	87.63 34	99.12 64	92.14 100	98.69 39	97.94 99

CDPH-MVS		92.83 94	92.30 103	94.44 50	97.79 59	86.11 54	94.06 234	96.66 106	80.09 365	92.77 102	96.63 94	86.62 46	99.04 70	87.40 196	98.66 45	98.17 75

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

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

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
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

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

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

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

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

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
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

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
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

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

EPNet		91.79 114	91.02 139	94.10 65	90.10 421	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
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_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
DP-MVS Recon		91.95 111	91.28 132	93.96 69	98.33 34	85.92 62	94.66 182	96.66 106	82.69 311	90.03 191	95.82 146	82.30 113	99.03 71	84.57 242	96.48 131	96.91 196

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

test_fmvsmconf0.1_n		94.20 47	94.31 43	93.88 71	92.46 335	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

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
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

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

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

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

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

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

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

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

test_fmvsmconf0.01_n		93.19 86	93.02 87	93.71 81	89.25 434	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

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

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

QAPM		89.51 199	88.15 226	93.59 84	94.92 183	84.58 94	96.82 34	96.70 104	78.43 393	83.41 360	96.19 114	73.18 273	99.30 49	77.11 370	96.54 128	96.89 197

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

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

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

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
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

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

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
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

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

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

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

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	62.09 402	98.64 131	90.95 132	92.62 250	97.93 107

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

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

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	82.95 378	94.96 283

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

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

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

新几何1				93.10 103	97.30 77	84.35 109		95.56 221	71.09 471	91.26 152	96.24 108	82.87 103	98.86 103	79.19 347	98.10 76	96.07 238

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

OpenMVS	83.78 11	88.74 229	87.29 248	93.08 105	92.70 329	85.39 79	96.57 40	96.43 122	78.74 387	80.85 393	96.07 124	69.64 322	99.01 76	78.01 361	96.65 126	94.83 291

MAR-MVS		90.30 171	89.37 187	93.07 107	96.61 92	84.48 100	95.68 107	95.67 211	82.36 316	87.85 234	92.85 287	76.63 211	98.80 112	80.01 327	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
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

lupinMVS		90.92 150	90.21 158	93.03 108	93.86 270	83.88 121	92.81 311	93.86 330	79.84 368	91.76 138	94.29 233	77.92 195	98.04 202	90.48 143	97.11 108	97.17 168

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

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

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

fmvsm_s_conf0.1_n_a		93.19 86	93.26 80	92.97 113	92.49 333	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

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

LFMVS		90.08 178	89.13 193	92.95 115	96.71 88	82.32 186	96.08 69	89.91 447	86.79 189	92.15 122	96.81 84	62.60 400	98.34 165	87.18 200	93.90 201	98.19 73

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
jason		90.80 152	90.10 162	92.90 117	93.04 310	83.53 133	93.08 295	94.15 319	80.22 362	91.41 148	94.91 200	76.87 205	97.93 222	90.28 144	96.90 117	97.24 161
jason: jason.
DP-MVS		87.25 283	85.36 322	92.90 117	97.65 65	83.24 142	94.81 170	92.00 388	74.99 438	81.92 382	95.00 195	72.66 278	99.05 68	66.92 454	92.33 255	96.40 219

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

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

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

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

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

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

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

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

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

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

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

VDDNet		89.56 198	88.49 217	92.76 129	95.07 172	82.09 190	96.30 47	93.19 354	81.05 356	91.88 131	96.86 80	61.16 418	98.33 167	88.43 180	92.49 254	97.84 118

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

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	78.05 435	96.60 212

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

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
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

PCF-MVS	84.11 10	87.74 257	86.08 295	92.70 137	94.02 259	84.43 104	89.27 421	95.87 193	73.62 453	84.43 328	94.33 230	78.48 188	98.86 103	70.27 428	94.45 184	94.81 292
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
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

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

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

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

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

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	89.37 301	96.94 193

LS3D		87.89 252	86.32 284	92.59 144	96.07 119	82.92 161	95.23 137	94.92 278	75.66 430	82.89 368	95.98 131	72.48 282	99.21 56	68.43 442	95.23 164	95.64 258

Anonymous20240529		88.09 248	86.59 273	92.58 145	96.53 98	81.92 197	95.99 79	95.84 195	74.11 448	89.06 209	95.21 185	61.44 410	98.81 111	83.67 259	87.47 332	97.01 187

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

CPTT-MVS		91.99 110	91.80 110	92.55 147	98.24 38	81.98 194	96.76 35	96.49 120	81.89 333	90.24 180	96.44 103	78.59 182	98.61 137	89.68 159	97.85 89	97.06 181

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

114514_t		89.51 199	88.50 215	92.54 148	98.11 43	81.99 193	95.16 147	96.36 129	70.19 475	85.81 281	95.25 181	76.70 209	98.63 133	82.07 286	96.86 120	97.00 188

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

mamba_0408		89.06 219	87.92 233	92.50 151	94.76 194	82.66 171	79.84 498	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

DeepPCF-MVS	89.96 1	94.20 47	94.77 31	92.49 152	96.52 99	80.00 279	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

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

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

API-MVS		90.66 162	90.07 164	92.45 155	96.36 104	84.57 95	96.06 73	95.22 253	82.39 314	89.13 206	94.27 236	80.32 145	98.46 149	80.16 325	96.71 124	94.33 315

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	94.18 319

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	94.18 319

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	94.18 319

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

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

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

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

AdaColmap		89.89 188	89.07 196	92.37 160	97.41 72	83.03 156	94.42 198	95.92 185	82.81 308	86.34 270	94.65 217	73.89 260	99.02 74	80.69 314	95.51 152	95.05 278

CNLPA		89.07 218	87.98 230	92.34 164	96.87 85	84.78 90	94.08 231	93.24 351	81.41 347	84.46 326	95.13 191	75.57 231	96.62 342	77.21 368	93.84 204	95.61 261

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

ET-MVSNet_ETH3D		87.51 271	85.91 303	92.32 166	93.70 284	83.93 119	92.33 332	90.94 422	84.16 268	72.09 474	92.52 300	69.90 317	95.85 394	89.20 167	88.36 319	97.17 168

E4		91.74 122	91.55 119	92.31 167	94.27 245	80.80 245	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

E2		91.79 114	91.61 114	92.31 167	94.49 223	80.86 241	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

Anonymous202405211		87.68 258	86.13 291	92.31 167	96.66 90	80.74 247	94.87 164	91.49 405	80.47 361	89.46 202	95.44 169	54.72 459	98.23 173	82.19 282	89.89 291	97.97 96

E3		91.78 117	91.61 114	92.30 170	94.48 224	80.86 241	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

CHOSEN 1792x2688		88.84 225	87.69 238	92.30 170	96.14 110	81.42 215	90.01 407	95.86 194	74.52 443	87.41 244	93.94 249	75.46 232	98.36 162	80.36 320	95.53 151	97.12 177

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

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	81.52 346	86.93 251	92.79 293	78.32 190	98.23 173	79.93 328	90.55 278	95.88 247

CDS-MVSNet		89.45 202	88.51 214	92.29 172	93.62 287	83.61 132	93.01 299	94.68 294	81.95 328	87.82 237	93.24 276	78.69 180	96.99 319	80.34 321	93.23 229	96.28 225
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 181	89.27 192	92.29 172	95.78 135	80.95 234	92.68 316	96.22 147	81.91 330	86.66 261	93.75 261	82.23 115	98.44 155	79.40 346	94.79 171	97.48 147

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

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

E5new		91.71 124	91.55 119	92.20 178	94.33 238	80.62 251	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	80.62 251	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	80.62 251	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	80.62 251	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

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	79.71 370	84.01 342	94.18 239	76.68 210	98.75 117	77.28 367	93.41 223	95.02 279
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPP-MVSNet		91.70 128	91.56 118	92.13 183	95.88 131	80.50 258	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

patch_mono-2		93.74 65	94.32 41	92.01 184	97.54 67	78.37 332	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

原ACMM1				92.01 184	97.34 74	81.05 229		96.81 89	78.89 381	90.45 174	95.92 136	82.65 106	98.84 107	80.68 315	98.26 63	96.14 232

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	80.84 412	94.12 323

MG-MVS		91.77 119	91.70 112	92.00 187	97.08 82	80.03 277	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

EIA-MVS		91.95 111	91.94 108	91.98 188	95.16 168	80.01 278	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

PVSNet_Blended		90.73 156	90.32 156	91.98 188	96.12 112	81.25 219	92.55 321	96.83 84	82.04 326	89.10 207	92.56 299	81.04 138	98.85 105	86.72 208	95.91 141	95.84 249

guyue		91.12 145	90.84 144	91.96 190	94.59 212	80.57 256	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

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	94.29 316

TAMVS		89.21 212	88.29 223	91.96 190	93.71 282	82.62 176	93.30 284	94.19 316	82.22 320	87.78 238	93.94 249	78.83 177	96.95 322	77.70 363	92.98 237	96.32 222

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	89.85 293	95.63 259

FA-MVS(test-final)		89.66 194	88.91 204	91.93 193	94.57 216	80.27 262	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

MVS_Test		91.31 137	91.11 135	91.93 193	94.37 233	80.14 267	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

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	80.04 407	93.29 274	79.02 175	97.05 315	81.71 297	80.05 422	94.59 299

HyFIR lowres test		88.09 248	86.81 261	91.93 193	96.00 123	80.63 249	90.01 407	95.79 198	73.42 455	87.68 240	92.10 317	73.86 261	97.96 218	80.75 313	91.70 260	97.19 167

GeoE		90.05 179	89.43 184	91.90 198	95.16 168	80.37 261	95.80 96	94.65 295	83.90 274	87.55 243	94.75 209	78.18 191	97.62 246	81.28 303	93.63 213	97.71 130

thisisatest0530		88.67 230	87.61 240	91.86 199	94.87 187	80.07 272	94.63 183	89.90 448	84.00 272	88.46 221	93.78 258	66.88 356	98.46 149	83.30 261	92.65 245	97.06 181

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	94.28 317

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	81.02 408	94.59 299

AstraMVS		90.69 158	90.30 157	91.84 202	93.81 273	79.85 286	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

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	90.18 285	93.65 357
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
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	89.99 287	94.63 296

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	81.02 408	94.49 309

diffmvs		91.37 136	91.23 133	91.77 206	93.09 304	80.27 262	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
diffmvs_AUTHOR		91.51 132	91.44 125	91.73 207	93.09 304	80.27 262	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

1112_ss		88.42 237	87.33 247	91.72 208	94.92 183	80.98 232	92.97 303	94.54 299	78.16 399	83.82 345	93.88 254	78.78 179	97.91 224	79.45 342	89.41 300	96.26 226

Fast-Effi-MVS+		89.41 206	88.64 210	91.71 209	94.74 197	80.81 244	93.54 270	95.10 260	83.11 297	86.82 259	90.67 371	79.74 161	97.75 237	80.51 318	93.55 215	96.57 215

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	90.89 274	97.24 161

TAPA-MVS	84.62 6	88.16 246	87.01 256	91.62 211	96.64 91	80.65 248	94.39 205	96.21 150	76.38 422	86.19 274	95.44 169	79.75 160	98.08 194	62.75 472	95.29 161	96.13 233
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
onestephybrid01		91.23 138	91.10 137	91.61 212	93.07 306	79.86 284	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

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	82.86 383	94.52 304

viewmamba		91.38 134	91.32 129	91.58 214	93.02 313	79.63 295	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

FE-MVS		87.40 276	86.02 297	91.57 215	94.56 217	79.69 294	90.27 394	93.72 341	80.57 359	88.80 215	91.62 337	65.32 372	98.59 139	74.97 393	94.33 189	96.44 218

hybridnocas07		90.93 149	90.72 148	91.54 216	92.75 326	79.72 292	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

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	89.96 289	95.83 250

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	77.07 443	95.74 254

TranMVSNet+NR-MVSNet		88.84 225	87.95 231	91.49 219	92.68 330	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	79.87 425	94.50 307

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	77.12 442	95.73 256

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	89.64 298	95.91 244

MVS		87.44 274	86.10 294	91.44 222	92.61 332	83.62 130	92.63 318	95.66 213	67.26 483	81.47 385	92.15 312	77.95 194	98.22 175	79.71 331	95.48 154	92.47 408

hybrid		90.69 158	90.45 153	91.43 223	92.67 331	79.42 303	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

viewdifsd2359ckpt07		91.11 146	91.02 139	91.41 224	94.21 250	78.37 332	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

F-COLMAP		87.95 251	86.80 262	91.40 225	96.35 105	80.88 238	94.73 177	95.45 232	79.65 371	82.04 380	94.61 218	71.13 296	98.50 143	76.24 380	91.05 271	94.80 293

dcpmvs_2		93.49 70	94.19 52	91.38 226	97.69 64	76.78 376	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

thisisatest0515		87.33 279	85.99 298	91.37 227	93.49 290	79.55 296	90.63 386	89.56 456	80.17 363	87.56 242	90.86 361	67.07 353	98.28 171	81.50 299	93.02 236	96.29 224

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	89.77 296	94.50 307

fmvsm_s_conf0.5_n_7		93.15 89	93.76 68	91.31 229	94.42 231	79.48 298	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

RRT-MVS		90.85 151	90.70 149	91.30 230	94.25 247	76.83 375	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

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	78.23 358	85.36 351	93.70 356

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	77.56 365	85.01 354	93.73 354

RPMNet		83.95 370	81.53 381	91.21 233	90.58 409	79.34 308	85.24 474	96.76 94	71.44 469	85.55 288	82.97 475	70.87 301	98.91 98	61.01 476	89.36 302	95.40 265

IB-MVS	80.51 15	85.24 346	83.26 365	91.19 234	92.13 344	79.86 284	91.75 353	91.29 411	83.28 294	80.66 397	88.49 421	61.28 412	98.46 149	80.99 309	79.46 429	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
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	90.43 280	93.93 334
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
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	89.23 305	93.77 350

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	89.23 305	93.77 350

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	88.86 310	94.08 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 232	87.78 237	91.11 239	94.96 180	77.81 351	95.35 126	89.69 451	85.09 247	88.05 231	94.59 221	66.93 354	98.48 145	83.27 262	92.13 257	97.03 184

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	78.23 358	85.36 351	93.79 345

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	78.23 358	85.36 351	93.79 345

FMVSNet1		85.85 331	84.11 352	91.08 240	92.81 323	83.10 150	95.14 148	94.94 273	81.64 341	82.68 370	91.64 333	59.01 434	96.34 372	75.37 387	83.78 367	93.79 345

Test_1112_low_res		87.65 260	86.51 277	91.08 240	94.94 182	79.28 312	91.77 352	94.30 311	76.04 428	83.51 355	92.37 304	77.86 197	97.73 238	78.69 353	89.13 307	96.22 227

PS-MVSNAJss		89.97 183	89.62 178	91.02 244	91.90 353	80.85 243	95.26 136	95.98 178	86.26 205	86.21 273	94.29 233	79.70 162	97.65 242	88.87 175	88.10 321	94.57 301

BH-RMVSNet		88.37 240	87.48 243	91.02 244	95.28 160	79.45 300	92.89 306	93.07 357	85.45 231	86.91 253	94.84 207	70.35 311	97.76 233	73.97 402	94.59 179	95.85 248

UniMVSNet_ETH3D		87.53 270	86.37 281	91.00 246	92.44 336	78.96 317	94.74 176	95.61 218	84.07 271	85.36 306	94.52 223	59.78 426	97.34 287	82.93 266	87.88 326	96.71 208

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	88.31 320	94.76 294

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	88.55 312	93.72 355
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 313	85.13 328	90.98 249	96.52 99	81.50 209	96.14 64	96.16 160	73.78 451	83.65 351	92.15 312	63.26 393	97.37 286	82.82 270	81.74 397	94.06 328

IMVS_0403		89.97 183	89.64 177	90.96 250	93.72 278	77.75 356	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

sss		88.93 224	88.26 225	90.94 251	94.05 258	80.78 246	91.71 354	95.38 238	81.55 345	88.63 218	93.91 253	75.04 236	95.47 413	82.47 275	91.61 261	96.57 215

IMVS_0407		89.85 190	89.51 181	90.88 252	93.72 278	77.75 356	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

dtuplus		89.78 193	89.43 184	90.85 253	92.83 322	77.91 345	92.32 334	94.97 270	82.33 318	90.20 182	95.53 164	78.56 184	97.38 285	85.15 229	92.95 238	97.24 161

viewmambaseed2359dif		90.04 180	89.78 174	90.83 254	92.85 321	77.92 344	92.23 338	95.01 264	81.90 331	90.20 182	95.45 168	79.64 169	97.34 287	87.52 195	93.17 230	97.23 165

sd_testset		88.59 234	87.85 236	90.83 254	96.00 123	80.42 260	92.35 329	94.71 292	88.73 108	86.85 257	95.20 186	67.31 348	96.43 366	79.64 334	89.85 293	95.63 259

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	87.86 327	92.35 415

cascas		86.43 321	84.98 331	90.80 257	92.10 346	80.92 236	90.24 398	95.91 187	73.10 458	83.57 354	88.39 422	65.15 374	97.46 265	84.90 234	91.43 263	94.03 330

ECVR-MVS		89.09 217	88.53 213	90.77 258	95.62 145	75.89 389	96.16 60	84.22 486	87.89 150	90.20 182	96.65 91	63.19 395	98.10 183	85.90 219	96.94 114	98.33 51

GA-MVS		86.61 311	85.27 325	90.66 259	91.33 376	78.71 321	90.40 393	93.81 336	85.34 235	85.12 309	89.57 402	61.25 413	97.11 308	80.99 309	89.59 299	96.15 231

thres600view7		87.65 260	86.67 268	90.59 260	96.08 118	78.72 319	94.88 163	91.58 401	87.06 180	88.08 229	92.30 307	68.91 338	98.10 183	70.05 435	91.10 266	94.96 283

thres400		87.62 265	86.64 269	90.57 261	95.99 126	78.64 322	94.58 185	91.98 390	86.94 186	88.09 227	91.77 329	69.18 334	98.10 183	70.13 432	91.10 266	94.96 283

baseline1		88.10 247	87.28 249	90.57 261	94.96 180	80.07 272	94.27 215	91.29 411	86.74 191	87.41 244	94.00 246	76.77 208	96.20 377	80.77 312	79.31 431	95.44 263

viewdifsd2359ckpt11		89.43 204	89.05 198	90.56 263	92.89 319	77.00 371	92.81 311	94.52 300	87.03 181	89.77 194	95.79 149	74.67 244	97.51 255	88.97 171	84.98 355	97.17 168

viewmsd2359difaftdt		89.43 204	89.05 198	90.56 263	92.89 319	77.00 371	92.81 311	94.52 300	87.03 181	89.77 194	95.79 149	74.67 244	97.51 255	88.97 171	84.98 355	97.17 168

usedtu_dtu_shiyan1		86.84 300	85.61 314	90.53 265	90.50 413	81.80 201	90.97 378	94.96 271	83.05 299	83.50 356	90.32 378	72.15 286	96.65 336	79.49 339	85.55 349	93.15 380

FE-MVSNET3		86.84 300	85.61 314	90.53 265	90.50 413	81.80 201	90.97 378	94.96 271	83.05 299	83.50 356	90.32 378	72.15 286	96.65 336	79.49 339	85.55 349	93.15 380

FC-MVSNet-test		90.27 172	90.18 160	90.53 265	93.71 282	79.85 286	95.77 100	97.59 6	89.31 83	86.27 271	94.67 215	81.93 125	97.01 318	84.26 246	88.09 323	94.71 295

PAPM		86.68 310	85.39 320	90.53 265	93.05 309	79.33 311	89.79 410	94.77 290	78.82 384	81.95 381	93.24 276	76.81 206	97.30 290	66.94 452	93.16 231	94.95 287

WR-MVS		88.38 239	87.67 239	90.52 269	93.30 296	80.18 265	93.26 287	95.96 182	88.57 116	85.47 295	92.81 291	76.12 216	96.91 325	81.24 304	82.29 388	94.47 312

SSM_04072		88.57 236	87.92 233	90.51 270	94.76 194	82.66 171	79.84 498	94.64 296	85.18 237	88.96 211	95.00 195	76.00 219	92.03 465	83.74 256	93.15 232	96.85 200

MVSTER		88.84 225	88.29 223	90.51 270	92.95 316	80.44 259	93.73 260	95.01 264	84.66 262	87.15 248	93.12 281	72.79 277	97.21 301	87.86 187	87.36 335	93.87 339

testdata				90.49 272	96.40 102	77.89 348		95.37 241	72.51 463	93.63 80	96.69 87	82.08 121	97.65 242	83.08 263	97.39 103	95.94 243

test1111		89.10 215	88.64 210	90.48 273	95.53 151	74.97 399	96.08 69	84.89 484	88.13 133	90.16 188	96.65 91	63.29 392	98.10 183	86.14 214	96.90 117	98.39 46

tt0805		86.92 297	85.74 312	90.48 273	92.22 340	79.98 280	95.63 114	94.88 281	83.83 277	84.74 318	92.80 292	57.61 441	97.67 239	85.48 225	84.42 360	93.79 345

jajsoiax		88.24 244	87.50 242	90.48 273	90.89 397	80.14 267	95.31 128	95.65 215	84.97 250	84.24 337	94.02 244	65.31 373	97.42 272	88.56 178	88.52 314	93.89 335

PatchMatch-RL		86.77 307	85.54 316	90.47 276	95.88 131	82.71 169	90.54 389	92.31 378	79.82 369	84.32 334	91.57 341	68.77 340	96.39 368	73.16 408	93.48 221	92.32 416

0.4-1-1-0.1		81.55 402	78.59 425	90.42 277	87.55 456	79.90 282	88.56 434	89.19 461	77.01 414	79.72 414	77.71 491	54.84 456	97.11 308	80.50 319	72.20 457	94.26 318

tfpn200view9		87.58 268	86.64 269	90.41 278	95.99 126	78.64 322	94.58 185	91.98 390	86.94 186	88.09 227	91.77 329	69.18 334	98.10 183	70.13 432	91.10 266	94.48 310

VPNet		88.20 245	87.47 244	90.39 279	93.56 289	79.46 299	94.04 235	95.54 224	88.67 111	86.96 250	94.58 222	69.33 328	97.15 303	84.05 250	80.53 417	94.56 302

ACMH	80.38 17	85.36 341	83.68 359	90.39 279	94.45 228	80.63 249	94.73 177	94.85 283	82.09 322	77.24 442	92.65 296	60.01 424	97.58 249	72.25 413	84.87 357	92.96 387
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 263	86.71 265	90.38 281	96.12 112	78.55 325	95.03 155	91.58 401	87.15 175	88.06 230	92.29 308	68.91 338	98.10 183	70.13 432	91.10 266	94.48 310

mvs_tets		88.06 250	87.28 249	90.38 281	90.94 393	79.88 283	95.22 139	95.66 213	85.10 246	84.21 338	93.94 249	63.53 390	97.40 280	88.50 179	88.40 318	93.87 339

1314		87.51 271	86.57 274	90.34 283	92.42 337	79.74 291	92.63 318	95.35 243	78.35 394	80.14 404	91.62 337	74.05 256	97.15 303	81.05 305	93.53 217	94.12 323

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	79.63 416	92.67 295	69.69 321	97.79 231	71.20 419	86.26 344	91.72 426
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
0.3-1-1-0.015		80.75 415	77.58 430	90.25 285	86.55 461	79.72 292	87.46 455	89.48 459	76.43 421	77.93 437	75.94 494	52.31 468	97.05 315	80.25 324	71.85 461	93.99 332

test_djsdf		89.03 221	88.64 210	90.21 286	90.74 404	79.28 312	95.96 83	95.90 188	84.66 262	85.33 307	92.94 286	74.02 257	97.30 290	89.64 161	88.53 313	94.05 329

v2v482		87.84 253	87.06 253	90.17 287	90.99 389	79.23 315	94.00 241	95.13 257	84.87 253	85.53 290	92.07 320	74.45 248	97.45 266	84.71 241	81.75 396	93.85 342

pmmvs4		85.43 339	83.86 357	90.16 288	90.02 424	82.97 160	90.27 394	92.67 369	75.93 429	80.73 395	91.74 331	71.05 297	95.73 402	78.85 352	83.46 374	91.78 425

V42		87.68 258	86.86 258	90.15 289	90.58 409	80.14 267	94.24 218	95.28 249	83.66 281	85.67 285	91.33 343	74.73 242	97.41 278	84.43 245	81.83 394	92.89 390

MSDG		84.86 354	83.09 368	90.14 290	93.80 274	80.05 274	89.18 424	93.09 356	78.89 381	78.19 433	91.91 326	65.86 371	97.27 294	68.47 441	88.45 316	93.11 382

sc_t1		81.53 403	78.67 424	90.12 291	90.78 401	78.64 322	93.91 249	90.20 437	68.42 479	80.82 394	89.88 395	46.48 483	96.76 330	76.03 383	71.47 462	94.96 283

anonymousdsp		87.84 253	87.09 252	90.12 291	89.13 435	80.54 257	94.67 181	95.55 222	82.05 324	83.82 345	92.12 314	71.47 294	97.15 303	87.15 201	87.80 330	92.67 397

thres200		87.21 287	86.24 288	90.12 291	95.36 156	78.53 326	93.26 287	92.10 384	86.42 200	88.00 232	91.11 354	69.24 333	98.00 210	69.58 436	91.04 273	93.83 344

CR-MVSNet		85.35 342	83.76 358	90.12 291	90.58 409	79.34 308	85.24 474	91.96 392	78.27 396	85.55 288	87.87 432	71.03 298	95.61 405	73.96 403	89.36 302	95.40 265

nomal-1		86.20 325	84.90 334	90.11 295	92.72 328	80.88 238	89.79 410	91.03 418	82.96 304	83.49 358	88.82 414	62.88 398	94.38 431	81.35 301	91.05 271	95.07 276

0.4-1-1-0.2		80.84 414	77.77 428	90.06 296	86.18 465	79.35 306	86.75 461	89.54 457	76.23 426	78.59 432	75.46 497	55.03 455	96.99 319	80.11 326	72.05 459	93.85 342

v1144		87.61 266	86.79 263	90.06 296	91.01 388	79.34 308	93.95 244	95.42 237	83.36 292	85.66 286	91.31 346	74.98 238	97.42 272	83.37 260	82.06 390	93.42 366

XXY-MVS		87.65 260	86.85 259	90.03 298	92.14 343	80.60 255	93.76 257	95.23 251	82.94 305	84.60 320	94.02 244	74.27 250	95.49 412	81.04 306	83.68 370	94.01 331

Vis-MVSNet (Re-imp)		89.59 197	89.44 183	90.03 298	95.74 136	75.85 390	95.61 115	90.80 426	87.66 161	87.83 236	95.40 172	76.79 207	96.46 363	78.37 354	96.73 123	97.80 123

test2506		87.21 287	86.28 286	90.02 300	95.62 145	73.64 415	96.25 55	71.38 511	87.89 150	90.45 174	96.65 91	55.29 453	98.09 191	86.03 218	96.94 114	98.33 51

BH-untuned		88.60 233	88.13 227	90.01 301	95.24 164	78.50 328	93.29 285	94.15 319	84.75 258	84.46 326	93.40 268	75.76 226	97.40 280	77.59 364	94.52 182	94.12 323

v1192		87.25 283	86.33 283	90.00 302	90.76 403	79.04 316	93.80 255	95.48 227	82.57 312	85.48 294	91.18 350	73.38 271	97.42 272	82.30 279	82.06 390	93.53 360

v7n		86.81 302	85.76 310	89.95 303	90.72 405	79.25 314	95.07 151	95.92 185	84.45 265	82.29 374	90.86 361	72.60 281	97.53 253	79.42 345	80.52 418	93.08 384

testing91		87.11 292	86.18 289	89.92 304	94.43 230	75.38 398	91.53 360	92.27 380	86.48 197	86.50 262	90.24 381	61.19 416	97.53 253	82.10 284	90.88 275	96.84 203

IMVS_0404		87.60 267	86.84 260	89.89 305	93.72 278	77.75 356	88.56 434	95.34 244	85.53 227	79.98 408	94.49 224	66.54 364	94.64 426	84.75 236	92.65 245	97.28 156

v8		87.50 273	86.71 265	89.89 305	91.37 373	79.40 304	94.50 190	95.38 238	84.81 256	83.60 353	91.33 343	76.05 217	97.42 272	82.84 269	80.51 419	92.84 392

v10		87.25 283	86.38 280	89.85 307	91.19 379	79.50 297	94.48 191	95.45 232	83.79 279	83.62 352	91.19 348	75.13 234	97.42 272	81.94 289	80.60 414	92.63 399

baseline2		86.50 317	85.39 320	89.84 308	91.12 384	76.70 378	91.88 348	88.58 463	82.35 317	79.95 409	90.95 359	73.42 269	97.63 245	80.27 323	89.95 290	95.19 272

pm-mvs1		86.61 311	85.54 316	89.82 309	91.44 368	80.18 265	95.28 134	94.85 283	83.84 276	81.66 383	92.62 297	72.45 284	96.48 360	79.67 333	78.06 434	92.82 393

TR-MVS		86.78 304	85.76 310	89.82 309	94.37 233	78.41 330	92.47 323	92.83 363	81.11 355	86.36 268	92.40 303	68.73 341	97.48 261	73.75 406	89.85 293	93.57 359

ACMH+	81.04 14	85.05 349	83.46 362	89.82 309	94.66 206	79.37 305	94.44 196	94.12 322	82.19 321	78.04 435	92.82 290	58.23 437	97.54 252	73.77 405	82.90 382	92.54 405

EI-MVSNet		89.10 215	88.86 207	89.80 312	91.84 355	78.30 335	93.70 264	95.01 264	85.73 218	87.15 248	95.28 179	79.87 159	97.21 301	83.81 254	87.36 335	93.88 338

gbinet_0.2-2-1-0.02		82.59 384	80.19 396	89.77 313	85.23 476	80.05 274	91.59 359	93.52 345	77.60 403	79.78 413	82.87 477	63.26 393	96.45 364	78.93 350	68.97 472	92.81 394

usedtu_blend_shiyan5		82.39 389	79.93 403	89.75 314	85.12 477	80.08 270	92.36 327	93.26 350	74.29 446	79.00 424	82.72 478	64.29 384	96.60 349	79.60 335	68.75 476	92.55 402

v144192		87.19 289	86.35 282	89.74 315	90.64 407	78.24 337	93.92 247	95.43 235	81.93 329	85.51 292	91.05 357	74.21 253	97.45 266	82.86 268	81.56 398	93.53 360

COLMAP_ROB	80.39 16	83.96 369	82.04 378	89.74 315	95.28 160	79.75 290	94.25 216	92.28 379	75.17 436	78.02 436	93.77 259	58.60 436	97.84 229	65.06 463	85.92 345	91.63 428
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 323	85.18 327	89.73 317	92.15 342	76.60 379	91.12 374	91.69 397	83.53 286	85.50 293	88.81 415	66.79 357	96.48 360	76.65 373	90.35 282	96.12 234

blend_shiyan4		81.94 392	79.35 411	89.70 318	85.52 472	80.08 270	91.29 368	93.82 333	77.12 412	79.31 420	82.94 476	54.81 457	96.60 349	79.60 335	69.78 467	92.41 411

IterMVS-LS		88.36 241	87.91 235	89.70 318	93.80 274	78.29 336	93.73 260	95.08 262	85.73 218	84.75 317	91.90 327	79.88 158	96.92 324	83.83 253	82.51 384	93.89 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blended_shiyan8		82.79 379	80.49 389	89.69 320	85.50 473	79.83 288	91.38 363	93.82 333	77.14 409	79.39 419	83.73 468	64.95 378	96.63 339	79.75 330	68.77 475	92.62 401

testing11		86.44 320	85.35 323	89.69 320	94.29 244	75.40 397	91.30 367	90.53 432	84.76 257	85.06 311	90.13 387	58.95 435	97.45 266	82.08 285	91.09 270	96.21 229

testing99		86.72 308	85.73 313	89.69 320	94.23 248	74.91 401	91.35 366	90.97 420	86.14 209	86.36 268	90.22 382	59.41 429	97.48 261	82.24 281	90.66 277	96.69 210

v1921920		86.97 296	86.06 296	89.69 320	90.53 412	78.11 340	93.80 255	95.43 235	81.90 331	85.33 307	91.05 357	72.66 278	97.41 278	82.05 287	81.80 395	93.53 360

icg_test_0407_2		89.15 213	88.97 200	89.68 324	93.72 278	77.75 356	88.26 440	95.34 244	85.53 227	88.34 224	94.49 224	77.69 199	93.99 440	84.75 236	92.65 245	97.28 156

blended_shiyan6		82.78 380	80.48 390	89.67 325	85.53 471	79.76 289	91.37 364	93.82 333	77.14 409	79.30 421	83.73 468	64.96 377	96.63 339	79.68 332	68.75 476	92.63 399

VortexMVS		88.42 237	88.01 229	89.63 326	93.89 269	78.82 318	93.82 253	95.47 228	86.67 194	84.53 324	91.99 323	72.62 280	96.65 336	89.02 170	84.09 364	93.41 367

Fast-Effi-MVS+-dtu		87.44 274	86.72 264	89.63 326	92.04 347	77.68 361	94.03 236	93.94 325	85.81 215	82.42 373	91.32 345	70.33 312	97.06 313	80.33 322	90.23 284	94.14 322

v1240		86.78 304	85.85 305	89.56 328	90.45 416	77.79 353	93.61 268	95.37 241	81.65 340	85.43 299	91.15 352	71.50 293	97.43 270	81.47 300	82.05 392	93.47 364

Effi-MVS+-dtu		88.65 231	88.35 219	89.54 329	93.33 295	76.39 383	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	94.97 280

wanda-best-256-512		82.44 386	80.07 398	89.53 330	85.12 477	79.44 301	90.49 390	93.75 339	76.97 415	79.00 424	82.72 478	64.29 384	96.61 345	79.56 337	68.75 476	92.55 402

FE-blended-shiyan7		82.44 386	80.07 398	89.53 330	85.12 477	79.44 301	90.49 390	93.75 339	76.97 415	79.00 424	82.72 478	64.29 384	96.61 345	79.56 337	68.75 476	92.55 402

AllTest		83.42 376	81.39 382	89.52 332	95.01 174	77.79 353	93.12 291	90.89 424	77.41 405	76.12 451	93.34 269	54.08 462	97.51 255	68.31 443	84.27 362	93.26 370

TestCases				89.52 332	95.01 174	77.79 353		90.89 424	77.41 405	76.12 451	93.34 269	54.08 462	97.51 255	68.31 443	84.27 362	93.26 370

mvs_anonymous		89.37 210	89.32 189	89.51 334	93.47 291	74.22 408	91.65 357	94.83 285	82.91 306	85.45 296	93.79 257	81.23 137	96.36 371	86.47 210	94.09 195	97.94 99

XVG-ACMP-BASELINE		86.00 327	84.84 337	89.45 335	91.20 378	78.00 342	91.70 355	95.55 222	85.05 248	82.97 367	92.25 310	54.49 460	97.48 261	82.93 266	87.45 334	92.89 390

testing222		84.84 355	83.32 363	89.43 336	94.15 255	75.94 388	91.09 375	89.41 460	84.90 251	85.78 282	89.44 404	52.70 467	96.28 375	70.80 426	91.57 262	96.07 238

MVP-Stereo		85.97 328	84.86 336	89.32 337	90.92 395	82.19 188	92.11 343	94.19 316	78.76 386	78.77 431	91.63 336	68.38 345	96.56 354	75.01 392	93.95 199	89.20 469
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 331	84.70 339	89.29 338	91.76 359	75.54 394	88.49 436	91.30 410	81.63 342	85.05 312	88.70 419	71.71 290	96.24 376	74.61 398	89.05 308	96.08 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 294	86.32 284	89.21 339	90.94 393	77.26 367	93.71 263	94.43 304	84.84 255	84.36 332	90.80 365	76.04 218	97.05 315	82.12 283	79.60 428	93.31 369

tfpnnormal		84.72 357	83.23 366	89.20 340	92.79 324	80.05 274	94.48 191	95.81 196	82.38 315	81.08 391	91.21 347	69.01 337	96.95 322	61.69 474	80.59 415	90.58 454

cl22		86.78 304	85.98 299	89.18 341	92.34 338	77.62 362	90.84 382	94.13 321	81.33 349	83.97 343	90.15 386	73.96 258	96.60 349	84.19 247	82.94 379	93.33 368

BH-w/o		87.57 269	87.05 254	89.12 342	94.90 186	77.90 347	92.41 324	93.51 346	82.89 307	83.70 349	91.34 342	75.75 227	97.07 312	75.49 385	93.49 219	92.39 413

WR-MVS_H		87.80 255	87.37 246	89.10 343	93.23 297	78.12 339	95.61 115	97.30 38	87.90 148	83.72 348	92.01 322	79.65 168	96.01 386	76.36 377	80.54 416	93.16 378

PRO-TEST		90.79 154	91.35 128	89.09 344	95.56 150	70.84 453	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

miper_enhance_ethall		86.90 298	86.18 289	89.06 345	91.66 364	77.58 363	90.22 400	94.82 286	79.16 377	84.48 325	89.10 408	79.19 173	96.66 335	84.06 249	82.94 379	92.94 388

c3_l		87.14 291	86.50 278	89.04 346	92.20 341	77.26 367	91.22 373	94.70 293	82.01 327	84.34 333	90.43 376	78.81 178	96.61 345	83.70 258	81.09 405	93.25 372

miper_ehance_all_eth		87.22 286	86.62 272	89.02 347	92.13 344	77.40 365	90.91 381	94.81 287	81.28 350	84.32 334	90.08 389	79.26 171	96.62 342	83.81 254	82.94 379	93.04 385

gg-mvs-nofinetune		81.77 396	79.37 410	88.99 348	90.85 399	77.73 360	86.29 465	79.63 497	74.88 441	83.19 366	69.05 509	60.34 421	96.11 381	75.46 386	94.64 178	93.11 382

ETVMVS		84.43 362	82.92 372	88.97 349	94.37 233	74.67 402	91.23 372	88.35 465	83.37 291	86.06 277	89.04 409	55.38 451	95.67 404	67.12 450	91.34 264	96.58 214

pmmvs6		83.42 376	81.60 380	88.87 350	88.01 451	77.87 349	94.96 158	94.24 315	74.67 442	78.80 430	91.09 355	60.17 423	96.49 359	77.06 372	75.40 449	92.23 418

test_cas_vis1_n_1920		88.83 228	88.85 208	88.78 351	91.15 383	76.72 377	93.85 252	94.93 277	83.23 296	92.81 100	96.00 129	61.17 417	94.45 427	91.67 117	94.84 170	95.17 273

MIMVSNet		82.59 384	80.53 387	88.76 352	91.51 366	78.32 334	86.57 464	90.13 440	79.32 373	80.70 396	88.69 420	52.98 466	93.07 456	66.03 458	88.86 310	94.90 288

cl____		86.52 316	85.78 307	88.75 353	92.03 348	76.46 381	90.74 383	94.30 311	81.83 336	83.34 362	90.78 366	75.74 229	96.57 352	81.74 295	81.54 399	93.22 374

DIV-MVS_self_test		86.53 315	85.78 307	88.75 353	92.02 349	76.45 382	90.74 383	94.30 311	81.83 336	83.34 362	90.82 364	75.75 227	96.57 352	81.73 296	81.52 400	93.24 373

CP-MVSNet		87.63 263	87.26 251	88.74 355	93.12 302	76.59 380	95.29 132	96.58 112	88.43 119	83.49 358	92.98 285	75.28 233	95.83 395	78.97 349	81.15 404	93.79 345

eth_miper_zixun_eth		86.50 317	85.77 309	88.68 356	91.94 350	75.81 391	90.47 392	94.89 279	82.05 324	84.05 340	90.46 375	75.96 221	96.77 329	82.76 272	79.36 430	93.46 365

CHOSEN 280x420		85.15 347	83.99 355	88.65 357	92.47 334	78.40 331	79.68 500	92.76 366	74.90 440	81.41 387	89.59 401	69.85 320	95.51 409	79.92 329	95.29 161	92.03 421

PS-CasMVS		87.32 280	86.88 257	88.63 358	92.99 314	76.33 385	95.33 127	96.61 110	88.22 129	83.30 364	93.07 283	73.03 275	95.79 399	78.36 355	81.00 410	93.75 352

TransMVSNet (Re)		84.43 362	83.06 370	88.54 359	91.72 360	78.44 329	95.18 145	92.82 365	82.73 310	79.67 415	92.12 314	73.49 266	95.96 388	71.10 423	68.73 480	91.21 441

tt0320-xc		79.63 429	76.66 438	88.52 360	91.03 387	78.72 319	93.00 300	89.53 458	66.37 486	76.11 453	87.11 443	46.36 485	95.32 417	72.78 410	67.67 481	91.51 433

EG-PatchMatch MVS		82.37 390	80.34 392	88.46 361	90.27 418	79.35 306	92.80 314	94.33 310	77.14 409	73.26 470	90.18 385	47.47 480	96.72 331	70.25 429	87.32 337	89.30 466

PEN-MVS		86.80 303	86.27 287	88.40 362	92.32 339	75.71 393	95.18 145	96.38 127	87.97 142	82.82 369	93.15 279	73.39 270	95.92 390	76.15 381	79.03 433	93.59 358

Baseline_NR-MVSNet		87.07 293	86.63 271	88.40 362	91.44 368	77.87 349	94.23 219	92.57 371	84.12 270	85.74 284	92.08 318	77.25 203	96.04 382	82.29 280	79.94 423	91.30 439

UBG		85.51 337	84.57 344	88.35 364	94.21 250	71.78 440	90.07 405	89.66 453	82.28 319	85.91 280	89.01 410	61.30 411	97.06 313	76.58 376	92.06 258	96.22 227

D2MVS		85.90 329	85.09 329	88.35 364	90.79 400	77.42 364	91.83 351	95.70 208	80.77 358	80.08 406	90.02 391	66.74 359	96.37 369	81.88 291	87.97 325	91.26 440

pmmvs5		84.21 365	82.84 375	88.34 366	88.95 437	76.94 373	92.41 324	91.91 394	75.63 431	80.28 401	91.18 350	64.59 381	95.57 406	77.09 371	83.47 373	92.53 406

tt0320		80.13 421	77.41 431	88.29 367	90.50 413	78.02 341	93.10 294	90.71 429	66.06 489	76.75 446	86.97 444	49.56 475	95.40 414	71.65 414	71.41 463	91.46 436

LCM-MVSNet-Re		88.30 243	88.32 222	88.27 368	94.71 202	72.41 435	93.15 290	90.98 419	87.77 155	79.25 422	91.96 324	78.35 189	95.75 400	83.04 264	95.62 149	96.65 211

CostFormer		85.77 334	84.94 333	88.26 369	91.16 382	72.58 433	89.47 419	91.04 417	76.26 425	86.45 266	89.97 393	70.74 303	96.86 328	82.35 278	87.07 340	95.34 269

ITE_SJBPF				88.24 370	91.88 354	77.05 370		92.92 360	85.54 225	80.13 405	93.30 273	57.29 442	96.20 377	72.46 412	84.71 358	91.49 434

PVSNet	78.82 18	85.55 336	84.65 340	88.23 371	94.72 200	71.93 436	87.12 458	92.75 367	78.80 385	84.95 314	90.53 373	64.43 382	96.71 333	74.74 395	93.86 202	96.06 240

IterMVS-SCA-FT		85.45 338	84.53 345	88.18 372	91.71 361	76.87 374	90.19 402	92.65 370	85.40 234	81.44 386	90.54 372	66.79 357	95.00 423	81.04 306	81.05 406	92.66 398

EPNet_dtu		86.49 319	85.94 302	88.14 373	90.24 419	72.82 425	94.11 226	92.20 382	86.66 195	79.42 418	92.36 305	73.52 265	95.81 397	71.26 418	93.66 212	95.80 252
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Patchmtry		82.71 382	80.93 386	88.06 374	90.05 423	76.37 384	84.74 480	91.96 392	72.28 466	81.32 389	87.87 432	71.03 298	95.50 411	68.97 438	80.15 421	92.32 416

test_vis1_n_1920		89.39 209	89.84 171	88.04 375	92.97 315	72.64 430	94.71 179	96.03 176	86.18 207	91.94 129	96.56 99	61.63 406	95.74 401	93.42 66	95.11 165	95.74 254

DTE-MVSNet		86.11 326	85.48 318	87.98 376	91.65 365	74.92 400	94.93 160	95.75 201	87.36 170	82.26 375	93.04 284	72.85 276	95.82 396	74.04 401	77.46 439	93.20 376

PMMVS		85.71 335	84.96 332	87.95 377	88.90 438	77.09 369	88.68 432	90.06 442	72.32 465	86.47 263	90.76 367	72.15 286	94.40 430	81.78 294	93.49 219	92.36 414

GG-mvs-BLEND				87.94 378	89.73 430	77.91 345	87.80 446	78.23 502		80.58 398	83.86 466	59.88 425	95.33 416	71.20 419	92.22 256	90.60 453

MonoMVSNet		86.89 299	86.55 275	87.92 379	89.46 433	73.75 412	94.12 224	93.10 355	87.82 154	85.10 310	90.76 367	69.59 323	94.94 424	86.47 210	82.50 385	95.07 276

reproduce_monomvs		86.37 322	85.87 304	87.87 380	93.66 286	73.71 413	93.44 275	95.02 263	88.61 114	82.64 372	91.94 325	57.88 439	96.68 334	89.96 151	79.71 427	93.22 374

pmmvs-eth3d		80.97 412	78.72 423	87.74 381	84.99 480	79.97 281	90.11 404	91.65 399	75.36 433	73.51 468	86.03 454	59.45 428	93.96 443	75.17 389	72.21 456	89.29 468

MS-PatchMatch		85.05 349	84.16 350	87.73 382	91.42 371	78.51 327	91.25 371	93.53 344	77.50 404	80.15 403	91.58 339	61.99 403	95.51 409	75.69 384	94.35 187	89.16 470

mmtdpeth		85.04 351	84.15 351	87.72 383	93.11 303	75.74 392	94.37 209	92.83 363	84.98 249	89.31 204	86.41 451	61.61 408	97.14 306	92.63 83	62.11 492	90.29 455

test_0402		81.30 408	79.17 416	87.67 384	93.19 298	78.17 338	92.98 302	91.71 395	75.25 435	76.02 454	90.31 380	59.23 430	96.37 369	50.22 498	83.63 371	88.47 479

IterMVS		84.88 353	83.98 356	87.60 385	91.44 368	76.03 387	90.18 403	92.41 373	83.24 295	81.06 392	90.42 377	66.60 360	94.28 435	79.46 341	80.98 411	92.48 407
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 406	79.30 412	87.58 386	90.92 395	74.16 410	80.99 493	87.68 470	70.52 473	76.63 448	88.81 415	71.21 295	92.76 460	60.01 481	86.93 341	95.83 250

EPMVS		83.90 372	82.70 376	87.51 387	90.23 420	72.67 428	88.62 433	81.96 492	81.37 348	85.01 313	88.34 423	66.31 365	94.45 427	75.30 388	87.12 338	95.43 264

ADS-MVSNet2		81.66 399	79.71 407	87.50 388	91.35 374	74.19 409	83.33 486	88.48 464	72.90 460	82.24 376	85.77 458	64.98 375	93.20 454	64.57 465	83.74 368	95.12 274

OurMVSNet-221017-0		85.35 342	84.64 342	87.49 389	90.77 402	72.59 432	94.01 239	94.40 307	84.72 259	79.62 417	93.17 278	61.91 404	96.72 331	81.99 288	81.16 402	93.16 378

tpm2		84.08 367	82.94 371	87.48 390	91.39 372	71.27 445	89.23 423	90.37 434	71.95 467	84.64 319	89.33 405	67.30 349	96.55 356	75.17 389	87.09 339	94.63 296

RPSCF		85.07 348	84.27 347	87.48 390	92.91 318	70.62 455	91.69 356	92.46 372	76.20 427	82.67 371	95.22 182	63.94 388	97.29 293	77.51 366	85.80 346	94.53 303

myMVS_eth3d28		85.80 333	85.26 326	87.42 392	94.73 198	69.92 461	90.60 387	90.95 421	87.21 174	86.06 277	90.04 390	59.47 427	96.02 384	74.89 394	93.35 227	96.33 221

FE-MVSNET2		81.82 395	79.99 401	87.34 393	84.74 481	77.36 366	92.72 315	94.55 298	82.09 322	73.79 467	86.46 448	57.80 440	94.45 427	74.65 396	73.10 451	90.20 456

WBMVS		84.97 352	84.18 349	87.34 393	94.14 256	71.62 444	90.20 401	92.35 375	81.61 343	84.06 339	90.76 367	61.82 405	96.52 357	78.93 350	83.81 366	93.89 335

miper_lstm_enhance		85.27 345	84.59 343	87.31 395	91.28 377	74.63 403	87.69 451	94.09 323	81.20 354	81.36 388	89.85 397	74.97 239	94.30 434	81.03 308	79.84 426	93.01 386

FMVSNet5		81.52 404	79.60 408	87.27 396	91.17 380	77.95 343	91.49 361	92.26 381	76.87 417	76.16 450	87.91 431	51.67 469	92.34 463	67.74 447	81.16 402	91.52 432

USDC		82.76 381	81.26 384	87.26 397	91.17 380	74.55 404	89.27 421	93.39 348	78.26 397	75.30 458	92.08 318	54.43 461	96.63 339	71.64 415	85.79 347	90.61 451

test-LLR		85.87 330	85.41 319	87.25 398	90.95 391	71.67 442	89.55 415	89.88 449	83.41 289	84.54 322	87.95 429	67.25 350	95.11 420	81.82 292	93.37 225	94.97 280

test-mter		84.54 361	83.64 360	87.25 398	90.95 391	71.67 442	89.55 415	89.88 449	79.17 376	84.54 322	87.95 429	55.56 448	95.11 420	81.82 292	93.37 225	94.97 280

JIA-IIPM		81.04 409	78.98 421	87.25 398	88.64 439	73.48 417	81.75 492	89.61 455	73.19 457	82.05 379	73.71 502	66.07 370	95.87 393	71.18 421	84.60 359	92.41 411

TDRefinement		79.81 425	77.34 432	87.22 401	79.24 499	75.48 395	93.12 291	92.03 387	76.45 420	75.01 459	91.58 339	49.19 476	96.44 365	70.22 431	69.18 471	89.75 462

tpmvs		83.35 378	82.07 377	87.20 402	91.07 386	71.00 451	88.31 439	91.70 396	78.91 379	80.49 400	87.18 441	69.30 331	97.08 310	68.12 446	83.56 372	93.51 363

ppachtmachnet_test		81.84 394	80.07 398	87.15 403	88.46 443	74.43 407	89.04 427	92.16 383	75.33 434	77.75 439	88.99 411	66.20 367	95.37 415	65.12 462	77.60 437	91.65 427

dmvs_re		84.20 366	83.22 367	87.14 404	91.83 357	77.81 351	90.04 406	90.19 438	84.70 261	81.49 384	89.17 407	64.37 383	91.13 477	71.58 416	85.65 348	92.46 409

tpm cat1		81.96 391	80.27 393	87.01 405	91.09 385	71.02 450	87.38 456	91.53 404	66.25 487	80.17 402	86.35 453	68.22 346	96.15 380	69.16 437	82.29 388	93.86 341

test_fmvs1_n		87.03 295	87.04 255	86.97 406	89.74 429	71.86 437	94.55 187	94.43 304	78.47 391	91.95 128	95.50 167	51.16 471	93.81 444	93.02 74	94.56 180	95.26 270

OpenMVS_ROB	74.94 19	79.51 430	77.03 437	86.93 407	87.00 458	76.23 386	92.33 332	90.74 428	68.93 477	74.52 463	88.23 426	49.58 474	96.62 342	57.64 487	84.29 361	87.94 482

SixPastTwentyTwo		83.91 371	82.90 373	86.92 408	90.99 389	70.67 454	93.48 272	91.99 389	85.54 225	77.62 441	92.11 316	60.59 420	96.87 327	76.05 382	77.75 436	93.20 376

ADS-MVSNet		81.56 401	79.78 404	86.90 409	91.35 374	71.82 438	83.33 486	89.16 462	72.90 460	82.24 376	85.77 458	64.98 375	93.76 445	64.57 465	83.74 368	95.12 274

PatchT		82.68 383	81.27 383	86.89 410	90.09 422	70.94 452	84.06 483	90.15 439	74.91 439	85.63 287	83.57 470	69.37 327	94.87 425	65.19 460	88.50 315	94.84 290

tpm		84.73 356	84.02 354	86.87 411	90.33 417	68.90 464	89.06 426	89.94 446	80.85 357	85.75 283	89.86 396	68.54 343	95.97 387	77.76 362	84.05 365	95.75 253

Patchmatch-RL test		81.67 398	79.96 402	86.81 412	85.42 474	71.23 446	82.17 491	87.50 472	78.47 391	77.19 443	82.50 482	70.81 302	93.48 449	82.66 273	72.89 454	95.71 257

test_vis1_n		86.56 314	86.49 279	86.78 413	88.51 440	72.69 427	94.68 180	93.78 338	79.55 372	90.70 168	95.31 178	48.75 477	93.28 452	93.15 70	93.99 198	94.38 314

testing3-2		86.72 308	86.71 265	86.74 414	96.11 115	65.92 477	93.39 277	89.65 454	89.46 76	87.84 235	92.79 293	59.17 432	97.60 247	81.31 302	90.72 276	96.70 209

test_fmvs1		87.34 278	87.56 241	86.68 415	90.59 408	71.80 439	94.01 239	94.04 324	78.30 395	91.97 126	95.22 182	56.28 446	93.71 446	92.89 75	94.71 173	94.52 304

MDA-MVSNet-bldmvs		78.85 435	76.31 440	86.46 416	89.76 428	73.88 411	88.79 430	90.42 433	79.16 377	59.18 497	88.33 424	60.20 422	94.04 438	62.00 473	68.96 473	91.48 435

mvs5depth		80.98 411	79.15 417	86.45 417	84.57 482	73.29 420	87.79 447	91.67 398	80.52 360	82.20 378	89.72 399	55.14 454	95.93 389	73.93 404	66.83 483	90.12 459

tpmrst		85.35 342	84.99 330	86.43 418	90.88 398	67.88 470	88.71 431	91.43 408	80.13 364	86.08 276	88.80 417	73.05 274	96.02 384	82.48 274	83.40 376	95.40 265

TESTMET0.1,1		83.74 374	82.85 374	86.42 419	89.96 425	71.21 447	89.55 415	87.88 467	77.41 405	83.37 361	87.31 437	56.71 444	93.65 448	80.62 316	92.85 242	94.40 313

our_test_3		81.93 393	80.46 391	86.33 420	88.46 443	73.48 417	88.46 437	91.11 413	76.46 419	76.69 447	88.25 425	66.89 355	94.36 432	68.75 439	79.08 432	91.14 443

lessismore_v0				86.04 421	88.46 443	68.78 465		80.59 495		73.01 472	90.11 388	55.39 450	96.43 366	75.06 391	65.06 487	92.90 389

TinyColmap		79.76 426	77.69 429	85.97 422	91.71 361	73.12 421	89.55 415	90.36 435	75.03 437	72.03 475	90.19 384	46.22 486	96.19 379	63.11 469	81.03 407	88.59 478

KD-MVS_2432*1600		78.50 436	76.02 444	85.93 423	86.22 463	74.47 405	84.80 478	92.33 376	79.29 374	76.98 444	85.92 455	53.81 464	93.97 441	67.39 448	57.42 497	89.36 464

miper_refine_blended		78.50 436	76.02 444	85.93 423	86.22 463	74.47 405	84.80 478	92.33 376	79.29 374	76.98 444	85.92 455	53.81 464	93.97 441	67.39 448	57.42 497	89.36 464

K. test v3		81.59 400	80.15 397	85.91 425	89.89 427	69.42 463	92.57 320	87.71 469	85.56 224	73.44 469	89.71 400	55.58 447	95.52 408	77.17 369	69.76 468	92.78 395

SSC-MVS3.2		84.60 360	84.19 348	85.85 426	92.74 327	68.07 467	88.15 442	93.81 336	87.42 168	83.76 347	91.07 356	62.91 397	95.73 402	74.56 399	83.24 377	93.75 352

mvsany_test1		85.42 340	85.30 324	85.77 427	87.95 453	75.41 396	87.61 454	80.97 494	76.82 418	88.68 217	95.83 145	77.44 202	90.82 480	85.90 219	86.51 342	91.08 447

MIMVSNet1		79.38 431	77.28 433	85.69 428	86.35 462	73.67 414	91.61 358	92.75 367	78.11 400	72.64 473	88.12 427	48.16 478	91.97 469	60.32 478	77.49 438	91.43 437

UWE-MVS		83.69 375	83.09 368	85.48 429	93.06 308	65.27 482	90.92 380	86.14 476	79.90 367	86.26 272	90.72 370	57.17 443	95.81 397	71.03 424	92.62 250	95.35 268

UnsupCasMVSNet_eth		80.07 422	78.27 427	85.46 430	85.24 475	72.63 431	88.45 438	94.87 282	82.99 303	71.64 478	88.07 428	56.34 445	91.75 471	73.48 407	63.36 490	92.01 422

CL-MVSNet_self_test		81.74 397	80.53 387	85.36 431	85.96 466	72.45 434	90.25 396	93.07 357	81.24 352	79.85 412	87.29 438	70.93 300	92.52 461	66.95 451	69.23 470	91.11 445

MDA-MVSNet_test_wron		79.21 433	77.19 435	85.29 432	88.22 448	72.77 426	85.87 468	90.06 442	74.34 444	62.62 494	87.56 435	66.14 368	91.99 468	66.90 455	73.01 452	91.10 446

YYNet1		79.22 432	77.20 434	85.28 433	88.20 449	72.66 429	85.87 468	90.05 444	74.33 445	62.70 492	87.61 434	66.09 369	92.03 465	66.94 452	72.97 453	91.15 442

WB-MVSnew		83.77 373	83.28 364	85.26 434	91.48 367	71.03 449	91.89 347	87.98 466	78.91 379	84.78 316	90.22 382	69.11 336	94.02 439	64.70 464	90.44 279	90.71 449

dp		81.47 405	80.23 394	85.17 435	89.92 426	65.49 480	86.74 462	90.10 441	76.30 424	81.10 390	87.12 442	62.81 399	95.92 390	68.13 445	79.88 424	94.09 326

UnsupCasMVSNet_bld		76.23 446	73.27 450	85.09 436	83.79 484	72.92 423	85.65 471	93.47 347	71.52 468	68.84 484	79.08 489	49.77 473	93.21 453	66.81 456	60.52 494	89.13 472

usedtu_dtu_shiyan2		74.72 448	71.30 453	84.98 437	77.78 501	70.58 456	91.85 350	90.76 427	67.24 484	68.06 486	82.17 483	37.13 495	92.78 459	60.69 477	66.03 484	91.59 431

SD_0403		84.71 358	84.65 340	84.92 438	92.95 316	65.95 476	92.07 346	93.23 352	83.82 278	79.03 423	93.73 262	73.90 259	92.91 458	63.02 471	90.05 286	95.89 246

Anonymous20231206		81.03 410	79.77 406	84.82 439	87.85 454	70.26 458	91.42 362	92.08 385	73.67 452	77.75 439	89.25 406	62.43 401	93.08 455	61.50 475	82.00 393	91.12 444

FE-MVSNET		78.19 438	76.03 443	84.69 440	83.70 485	73.31 419	90.58 388	90.00 445	77.11 413	71.91 476	85.47 460	55.53 449	91.94 470	59.69 482	70.24 465	88.83 474

test0.0.03 1		82.41 388	81.69 379	84.59 441	88.23 447	72.89 424	90.24 398	87.83 468	83.41 289	79.86 411	89.78 398	67.25 350	88.99 490	65.18 461	83.42 375	91.90 424

CMPMVS	59.16 21	80.52 416	79.20 415	84.48 442	83.98 483	67.63 473	89.95 409	93.84 332	64.79 491	66.81 488	91.14 353	57.93 438	95.17 418	76.25 379	88.10 321	90.65 450
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 359	84.79 338	84.37 443	91.84 355	64.92 483	93.70 264	91.47 407	66.19 488	86.16 275	95.28 179	67.18 352	93.33 451	80.89 311	90.42 281	94.88 289

PVSNet_0	73.20 20	77.22 442	74.83 448	84.37 443	90.70 406	71.10 448	83.09 488	89.67 452	72.81 462	73.93 466	83.13 472	60.79 419	93.70 447	68.54 440	50.84 504	88.30 480

LF4IMVS		80.37 419	79.07 419	84.27 445	86.64 459	69.87 462	89.39 420	91.05 416	76.38 422	74.97 460	90.00 392	47.85 479	94.25 436	74.55 400	80.82 413	88.69 476

Anonymous20240521		80.44 418	79.21 414	84.11 446	85.75 469	67.89 469	92.86 308	93.23 352	75.61 432	75.59 457	87.47 436	50.03 472	94.33 433	71.14 422	81.21 401	90.12 459

PM-MVS		78.11 439	76.12 442	84.09 447	83.54 486	70.08 459	88.97 428	85.27 483	79.93 366	74.73 462	86.43 450	34.70 498	93.48 449	79.43 344	72.06 458	88.72 475

dtuonly		84.33 364	84.48 346	83.87 448	86.63 460	63.54 488	86.79 460	91.48 406	78.02 401	83.20 365	93.56 265	69.53 325	94.11 437	79.08 348	92.02 259	93.97 333

test_fmvs2		83.98 368	84.03 353	83.83 449	87.16 457	67.53 474	93.93 246	92.89 361	77.62 402	86.89 256	93.53 266	47.18 481	92.02 467	90.54 140	86.51 342	91.93 423

testgi		80.94 413	80.20 395	83.18 450	87.96 452	66.29 475	91.28 369	90.70 430	83.70 280	78.12 434	92.84 288	51.37 470	90.82 480	63.34 468	82.46 386	92.43 410

KD-MVS_self_test		80.20 420	79.24 413	83.07 451	85.64 470	65.29 481	91.01 377	93.93 326	78.71 388	76.32 449	86.40 452	59.20 431	92.93 457	72.59 411	69.35 469	91.00 448

testing3		80.46 417	79.59 409	83.06 452	93.44 293	64.64 484	93.33 279	85.47 481	84.34 267	79.93 410	90.84 363	44.35 489	92.39 462	57.06 489	87.56 331	92.16 420

ambc				83.06 452	79.99 497	63.51 489	77.47 501	92.86 362		74.34 465	84.45 465	28.74 499	95.06 422	73.06 409	68.89 474	90.61 451

test20.03		79.95 424	79.08 418	82.55 454	85.79 468	67.74 472	91.09 375	91.08 414	81.23 353	74.48 464	89.96 394	61.63 406	90.15 482	60.08 479	76.38 445	89.76 461

MVStest1		72.91 451	69.70 456	82.54 455	78.14 500	73.05 422	88.21 441	86.21 475	60.69 495	64.70 490	90.53 373	46.44 484	85.70 498	58.78 485	53.62 500	88.87 473

test_vis1_rt		77.96 440	76.46 439	82.48 456	85.89 467	71.74 441	90.25 396	78.89 498	71.03 472	71.30 479	81.35 485	42.49 491	91.05 478	84.55 243	82.37 387	84.65 487

EU-MVSNet		81.32 407	80.95 385	82.42 457	88.50 442	63.67 487	93.32 280	91.33 409	64.02 492	80.57 399	92.83 289	61.21 415	92.27 464	76.34 378	80.38 420	91.32 438

myMVS_eth3d		79.67 427	78.79 422	82.32 458	91.92 351	64.08 485	89.75 413	87.40 473	81.72 338	78.82 428	87.20 439	45.33 487	91.29 475	59.09 484	87.84 328	91.60 429

ttmdpeth		76.55 444	74.64 449	82.29 459	82.25 491	67.81 471	89.76 412	85.69 479	70.35 474	75.76 455	91.69 332	46.88 482	89.77 484	66.16 457	63.23 491	89.30 466

dtuonlycased		79.67 427	79.05 420	81.54 460	88.34 446	68.44 466	88.96 429	90.65 431	78.48 390	73.21 471	85.88 457	63.18 396	91.00 479	70.40 427	72.32 455	85.19 486

pmmvs3		71.81 454	68.71 457	81.11 461	75.86 503	70.42 457	86.74 462	83.66 487	58.95 498	68.64 485	80.89 487	36.93 496	89.52 486	63.10 470	63.59 489	83.39 488

Syy-MVS		80.07 422	79.78 404	80.94 462	91.92 351	59.93 498	89.75 413	87.40 473	81.72 338	78.82 428	87.20 439	66.29 366	91.29 475	47.06 503	87.84 328	91.60 429

UWE-MVS-28		78.98 434	78.38 426	80.80 463	88.18 450	60.66 497	90.65 385	78.51 499	78.84 383	77.93 437	90.93 360	59.08 433	89.02 489	50.96 496	90.33 283	92.72 396

new-patchmatchnet		76.41 445	75.17 447	80.13 464	82.65 490	59.61 499	87.66 452	91.08 414	78.23 398	69.85 482	83.22 471	54.76 458	91.63 474	64.14 467	64.89 488	89.16 470

mvsany_test3		74.95 447	73.26 451	80.02 465	74.61 504	63.16 490	85.53 472	78.42 500	74.16 447	74.89 461	86.46 448	36.02 497	89.09 488	82.39 277	66.91 482	87.82 483

test_fmvs3		77.67 441	77.16 436	79.22 466	79.52 498	61.14 494	92.34 331	91.64 400	73.98 449	78.86 427	86.59 447	27.38 502	87.03 492	88.12 184	75.97 447	89.50 463

DSMNet-mixed		76.94 443	76.29 441	78.89 467	83.10 488	56.11 507	87.78 448	79.77 496	60.65 496	75.64 456	88.71 418	61.56 409	88.34 491	60.07 480	89.29 304	92.21 419

EGC-MVSNET		61.97 464	56.37 469	78.77 468	89.63 431	73.50 416	89.12 425	82.79 489	0.21 557	1.24 559	84.80 463	39.48 492	90.04 483	44.13 505	75.94 448	72.79 503

ArgMatch-SfM		70.39 455	67.69 459	78.49 469	81.44 493	60.73 495	84.71 481	75.65 509	68.09 481	66.71 489	86.79 445	20.42 508	86.05 497	71.50 417	53.87 499	88.67 477

new_pmnet		72.15 452	70.13 455	78.20 470	82.95 489	65.68 478	83.91 484	82.40 491	62.94 494	64.47 491	79.82 488	42.85 490	86.26 496	57.41 488	74.44 450	82.65 492

MVS-HIRNet		73.70 450	72.20 452	78.18 471	91.81 358	56.42 506	82.94 489	82.58 490	55.24 499	68.88 483	66.48 511	55.32 452	95.13 419	58.12 486	88.42 317	83.01 490

ArgMatch-Sym		69.79 456	67.05 461	77.99 472	81.59 492	61.16 493	84.99 477	71.84 510	67.17 485	67.90 487	86.60 446	19.89 511	85.00 500	70.93 425	52.57 501	87.82 483

LCM-MVSNet		66.00 461	62.16 466	77.51 473	64.51 519	58.29 501	83.87 485	90.90 423	48.17 504	54.69 500	73.31 503	16.83 513	86.75 493	65.47 459	61.67 493	87.48 485

APD_test1		69.04 457	66.26 463	77.36 474	80.51 496	62.79 491	85.46 473	83.51 488	54.11 501	59.14 498	84.79 464	23.40 505	89.61 485	55.22 490	70.24 465	79.68 497

test_f		71.95 453	70.87 454	75.21 475	74.21 507	59.37 500	85.07 476	85.82 478	65.25 490	70.42 481	83.13 472	23.62 503	82.93 505	78.32 356	71.94 460	83.33 489

ANet_high		58.88 468	54.22 473	72.86 476	56.50 526	56.67 503	80.75 494	86.00 477	73.09 459	37.39 518	64.63 515	22.17 506	79.49 509	43.51 507	23.96 523	82.43 493

test_vis3_rt		65.12 462	62.60 464	72.69 477	71.44 509	60.71 496	87.17 457	65.55 513	63.80 493	53.22 501	65.65 514	14.54 514	89.44 487	76.65 373	65.38 486	67.91 512

LoFTR		57.22 471	52.62 475	71.00 478	72.03 508	48.57 513	72.00 509	70.08 512	44.40 509	40.92 514	76.42 493	8.12 521	82.76 506	42.28 511	47.33 507	81.66 494

FPMVS		64.63 463	62.55 465	70.88 479	70.80 510	56.71 502	84.42 482	84.42 485	51.78 502	49.57 502	81.61 484	23.49 504	81.48 507	40.61 513	76.25 446	74.46 502

dmvs_testset		74.57 449	75.81 446	70.86 480	87.72 455	40.47 522	87.05 459	77.90 504	82.75 309	71.15 480	85.47 460	67.98 347	84.12 503	45.26 504	76.98 444	88.00 481

DenseAffine		56.77 472	52.17 476	70.54 481	74.27 505	53.25 509	77.23 502	50.43 521	49.87 503	47.26 507	77.37 492	7.99 522	79.10 510	50.35 497	34.79 514	79.28 498

N_pmnet		68.89 458	68.44 458	70.23 482	89.07 436	28.79 533	88.06 443	19.50 534	69.47 476	71.86 477	84.93 462	61.24 414	91.75 471	54.70 491	77.15 441	90.15 458

testf1		59.54 466	56.11 470	69.85 483	69.28 511	56.61 504	80.37 495	76.55 507	42.58 511	45.68 508	75.61 495	11.26 515	84.18 501	43.20 509	60.44 495	68.75 509

APD_test2		59.54 466	56.11 470	69.85 483	69.28 511	56.61 504	80.37 495	76.55 507	42.58 511	45.68 508	75.61 495	11.26 515	84.18 501	43.20 509	60.44 495	68.75 509

WB-MVS		67.92 459	67.49 460	69.21 485	81.09 494	41.17 521	88.03 444	78.00 503	73.50 454	62.63 493	83.11 474	63.94 388	86.52 494	25.66 523	51.45 503	79.94 496

PMMVS2		59.60 465	56.40 468	69.21 485	68.83 513	46.58 514	73.02 508	77.48 505	55.07 500	49.21 503	72.95 504	17.43 512	80.04 508	49.32 500	44.33 508	80.99 495

SSC-MVS		67.06 460	66.56 462	68.56 487	80.54 495	40.06 523	87.77 449	77.37 506	72.38 464	61.75 495	82.66 481	63.37 391	86.45 495	24.48 525	48.69 506	79.16 499

Gipuma		57.99 470	54.91 472	67.24 488	88.51 440	65.59 479	52.21 518	90.33 436	43.58 510	42.84 511	51.18 522	20.29 509	85.07 499	34.77 515	70.45 464	51.05 521
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RoMa-SfM		53.80 473	49.39 477	67.06 489	67.87 515	48.86 511	75.04 503	38.06 528	47.23 506	47.40 506	78.96 490	7.40 523	76.66 512	48.89 501	33.62 515	75.64 501

DKM		50.92 477	46.13 481	65.30 490	66.27 517	45.98 516	73.05 507	31.91 530	45.08 507	42.04 512	75.01 500	4.95 532	73.81 514	47.90 502	28.96 518	76.09 500

MatchFormer		51.11 476	46.66 480	64.46 491	67.11 516	43.39 519	70.54 510	63.67 515	33.19 517	37.22 519	70.30 507	6.67 526	78.17 511	30.29 519	40.94 510	71.81 506

PMVS	47.18 22	52.22 475	48.46 479	63.48 492	45.72 530	46.20 515	73.41 506	78.31 501	41.03 513	30.06 524	65.68 513	6.05 527	83.43 504	30.04 520	65.86 485	60.80 515
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		58.82 469	58.24 467	60.56 493	83.13 487	45.09 518	82.32 490	48.22 523	67.61 482	61.70 496	69.15 508	38.75 493	76.05 513	32.01 518	41.31 509	60.55 516

DKM-HiRes		45.90 481	41.41 486	59.36 494	59.55 522	39.90 524	67.13 511	23.25 532	39.95 515	38.74 516	71.81 506	3.67 541	66.42 521	43.82 506	24.82 520	71.77 507

RoMa-HiRes		46.47 480	42.20 485	59.28 495	57.74 524	39.86 525	66.76 512	24.64 531	39.96 514	41.50 513	75.37 498	5.40 529	69.26 515	43.35 508	25.09 519	68.71 511

PDCNetPlus		48.34 479	45.15 482	57.91 496	61.43 521	41.85 520	65.98 513	38.30 527	47.59 505	37.96 517	71.85 505	10.18 518	66.85 520	52.94 494	20.14 534	65.03 514

MVE	39.65 23	43.39 483	38.59 489	57.77 497	56.52 525	48.77 512	55.38 516	58.64 518	29.33 521	28.96 525	52.65 521	4.68 535	64.62 522	28.11 521	33.07 516	59.93 517
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		50.52 478	48.47 478	56.66 498	52.26 529	18.98 539	41.51 525	81.40 493	10.10 528	44.59 510	75.01 500	28.51 500	68.16 516	53.54 493	49.31 505	82.83 491

DeepMVS_CX				56.31 499	74.23 506	51.81 510		56.67 519	44.85 508	48.54 504	75.16 499	27.87 501	58.74 524	40.92 512	52.22 502	58.39 519

ELoFTR		40.15 486	35.08 490	55.36 500	41.27 537	28.17 535	47.70 520	43.76 524	29.15 522	30.35 523	65.97 512	2.17 543	66.90 519	34.51 516	20.83 533	71.00 508

kuosan		53.51 474	53.30 474	54.13 501	76.06 502	45.36 517	80.11 497	48.36 522	59.63 497	54.84 499	63.43 517	37.41 494	62.07 523	20.73 527	39.10 511	54.96 520

PMatch-SfM		38.18 487	33.34 491	52.72 502	43.67 532	28.18 534	52.96 517	16.29 538	29.70 520	31.24 522	68.56 510	1.08 556	57.70 525	38.73 514	17.80 537	72.30 505

MASt3R-SfM		45.78 482	43.96 483	51.24 503	45.04 531	29.83 532	57.88 515	38.83 526	31.88 519	47.48 505	81.30 486	7.16 524	51.15 527	49.56 499	36.51 512	72.74 504

GLUNet-SfM		31.36 490	26.25 497	46.70 504	35.51 540	24.89 536	33.71 530	36.36 529	19.08 524	23.78 529	52.69 520	3.82 540	56.26 526	19.75 529	11.56 548	58.95 518

E-PMN		43.23 484	42.29 484	46.03 505	65.58 518	37.41 526	73.51 505	64.62 514	33.99 516	28.47 526	47.87 524	19.90 510	67.91 517	22.23 526	24.45 521	32.77 527

PMatch-Up-SfM		32.59 489	28.46 494	44.98 506	37.19 538	22.27 538	44.73 523	10.63 545	23.85 523	27.52 527	64.10 516	0.78 560	47.14 528	34.15 517	13.22 544	65.53 513

EMVS		42.07 485	41.12 487	44.92 507	63.45 520	35.56 528	73.65 504	63.48 516	33.05 518	26.88 528	45.45 525	21.27 507	67.14 518	19.80 528	23.02 525	32.06 528

ALIKED-LG		28.00 491	26.54 496	32.41 508	58.12 523	31.80 529	47.26 521	21.21 533	14.15 525	19.16 531	41.93 527	6.72 525	35.73 530	5.96 539	24.32 522	29.69 529

ALIKED-MNN		26.28 493	24.57 499	31.39 509	56.22 527	31.73 530	45.54 522	19.13 536	11.12 526	17.11 534	39.35 529	5.01 531	34.53 531	5.54 541	22.12 527	27.92 530

ALIKED-NN		26.07 494	24.75 498	30.02 510	55.08 528	30.61 531	44.20 524	19.22 535	10.98 527	17.98 532	40.71 528	5.39 530	32.83 532	5.59 540	23.63 524	26.63 531

tmp_tt		35.64 488	39.24 488	24.84 511	14.87 559	23.90 537	62.71 514	51.51 520	6.58 538	36.66 520	62.08 519	44.37 488	30.34 534	52.40 495	22.00 528	20.27 534

wuyk23d		21.27 497	20.48 500	23.63 512	68.59 514	36.41 527	49.57 519	6.85 551	9.37 529	7.89 542	4.46 557	4.03 539	31.37 533	17.47 530	16.07 539	3.12 553

VLMVS_CLIP		27.58 492	28.97 493	23.41 513	23.47 555	13.17 547	30.64 531	40.90 525	9.21 530	36.34 521	50.75 523	8.75 520	38.05 529	25.18 524	35.53 513	19.03 536

SP-LightGlue		20.24 498	20.15 502	20.49 514	43.51 533	12.27 549	38.68 527	14.56 541	7.54 534	12.90 539	30.07 534	4.75 533	14.38 538	7.60 534	21.75 529	34.82 522

SP-SuperGlue		20.22 499	20.18 501	20.36 515	43.26 534	12.27 549	38.71 526	14.77 540	7.64 533	13.04 538	30.21 533	4.73 534	14.21 540	7.59 535	21.65 530	34.59 523

SP-DiffGlue		20.02 500	19.96 503	20.21 516	19.64 556	13.14 548	30.51 532	15.49 539	8.39 531	19.98 530	43.75 526	5.48 528	13.72 541	13.75 531	22.65 526	33.78 525

SP-MNN		19.61 501	19.42 504	20.19 517	42.15 535	11.42 555	38.15 528	14.24 542	6.55 539	11.64 541	29.88 536	4.16 537	14.56 537	7.09 537	20.92 532	34.58 524

SP-NN		19.44 502	19.37 505	19.67 518	41.70 536	11.48 554	37.75 529	13.72 544	6.86 535	11.86 540	29.97 535	4.23 536	14.25 539	7.13 536	21.07 531	33.30 526

XFeat-MNN		17.43 503	16.95 506	18.86 519	16.90 557	11.28 556	27.31 534	17.08 537	8.08 532	15.61 536	35.73 530	4.06 538	22.95 535	10.20 532	17.59 538	22.35 533

XFeat-NN		15.96 504	15.86 507	16.25 520	15.78 558	9.87 559	25.17 535	13.83 543	6.76 536	15.68 535	34.83 531	3.61 542	19.28 536	9.22 533	17.90 536	19.58 535

MVS_clip		24.79 495	27.71 495	16.02 521	35.36 541	15.85 541	27.38 533	5.39 557	6.70 537	40.04 515	63.09 518	10.55 517	8.72 555	27.86 522	33.03 517	23.49 532

SIFT-NN		12.98 505	13.18 508	12.37 522	36.49 539	16.03 540	22.41 536	7.69 547	4.89 540	7.41 543	20.48 539	1.69 544	11.46 543	1.88 545	15.70 540	9.61 539

SIFT-MNN		12.44 506	12.55 509	12.11 523	34.55 542	15.21 542	20.91 537	7.74 546	4.86 541	6.54 545	20.09 540	1.51 545	11.47 542	1.88 545	14.87 542	9.64 538

SIFT-NN-NCMNet		12.12 507	12.25 510	11.75 524	32.82 544	14.83 543	20.73 538	7.58 548	4.72 543	6.60 544	19.53 541	1.49 546	11.15 545	1.74 547	15.02 541	9.28 540

SIFT-NCM-Cal		11.58 508	11.64 512	11.40 525	33.45 543	14.10 544	19.75 540	6.89 549	4.68 546	4.55 552	18.60 546	1.34 550	11.28 544	1.53 553	13.95 543	8.82 545

SIFT-NN-CMatch		11.26 509	11.31 514	11.13 526	30.21 548	13.40 546	18.43 541	6.79 552	4.71 544	6.47 546	19.53 541	1.43 548	10.72 547	1.71 548	12.49 547	9.26 541

SIFT-ConvMatch		10.91 512	10.94 517	10.84 527	32.07 545	13.57 545	17.23 544	6.35 553	4.71 544	5.18 549	18.94 544	1.30 551	10.76 546	1.65 551	11.02 550	8.19 546

SIFT-NN-UMatch		11.06 510	11.19 516	10.66 528	28.66 550	12.16 551	19.79 539	6.86 550	4.73 542	5.21 548	19.47 543	1.46 547	10.70 548	1.71 548	12.79 546	9.13 542

SIFT-UMatch		10.58 513	10.73 518	10.15 529	31.05 546	11.65 553	18.01 542	5.92 555	4.65 547	4.72 550	18.93 545	1.25 553	10.62 549	1.66 550	10.39 551	8.16 547

SIFT-CM-Cal		10.08 515	10.13 521	9.92 530	30.71 547	11.88 552	15.35 546	5.44 556	4.59 548	4.72 550	18.04 549	1.26 552	10.19 550	1.46 555	9.60 552	7.69 548

SIFT-NN-PointCN		10.26 514	10.46 519	9.65 531	27.18 551	9.89 558	17.89 543	6.17 554	4.40 550	5.65 547	18.29 547	1.43 548	10.09 551	1.61 552	11.55 549	8.99 544

SIFT-UM-Cal		9.80 516	10.00 522	9.22 532	30.05 549	10.15 557	16.31 545	4.85 560	4.54 549	4.19 553	18.23 548	1.19 554	9.95 552	1.52 554	9.11 554	7.57 549

VLMVS		10.93 511	11.73 511	8.51 533	11.99 560	6.47 563	9.10 550	5.11 558	0.73 554	17.62 533	25.59 537	9.61 519	6.56 557	6.19 538	19.64 535	12.50 537

SIFT-PCN-Cal		8.65 520	8.88 524	7.98 534	26.74 552	7.47 561	13.90 548	4.61 561	4.09 552	3.82 554	15.86 550	1.01 557	8.94 553	1.34 556	8.52 555	7.53 550

SIFT-PointCN		8.76 518	9.03 523	7.96 535	26.50 553	7.60 560	14.94 547	5.08 559	4.10 551	3.74 555	15.46 551	0.94 558	8.92 554	1.33 557	9.14 553	7.37 551

SIFT-NCMNet		7.46 522	7.71 527	6.72 536	25.03 554	6.86 562	11.42 549	2.98 562	4.05 553	3.38 556	13.68 552	0.84 559	7.65 556	1.13 558	6.87 556	5.66 552

MVS_baseline		7.30 523	8.69 526	3.12 537	8.45 561	0.31 566	3.27 551	0.80 563	0.16 558	14.50 537	32.51 532	1.15 555	0.00 560	4.24 542	13.11 545	9.06 543

test123		8.76 518	11.22 515	1.39 538	0.85 563	0.97 564	85.76 470	0.35 565	0.54 556	2.45 558	8.14 556	0.60 561	0.48 558	2.16 544	0.17 558	2.71 554

testmvs		8.92 517	11.52 513	1.12 539	1.06 562	0.46 565	86.02 466	0.65 564	0.62 555	2.74 557	9.52 555	0.31 562	0.45 559	2.38 543	0.39 557	2.46 555

mmdepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

monomultidepth		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

test_blank		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uanet_test		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

DCPMVS		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

cdsmvs_eth3d_5k		22.14 496	29.52 492	0.00 540	0.00 564	0.00 567	0.00 552	95.76 200	0.00 559	0.00 560	94.29 233	75.66 230	0.00 560	0.00 559	0.00 559	0.00 556

pcd_1.5k_mvsjas		6.64 524	8.86 525	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	79.70 162	0.00 560	0.00 559	0.00 559	0.00 556

sosnet-low-res		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

sosnet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uncertanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

Regformer		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

ab-mvs-re		7.82 521	10.43 520	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	93.88 254	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

uanet		0.00 525	0.00 528	0.00 540	0.00 564	0.00 567	0.00 552	0.00 566	0.00 559	0.00 560	0.00 558	0.00 563	0.00 560	0.00 559	0.00 559	0.00 556

PatchmatchNet2					0.00 564	62.07 492	85.98 467	87.63 471	68.79 478
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet1														54.59 492	77.20 440	90.17 457
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													91.68 473
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
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

WAC-MVS						64.08 485								59.14 483

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

PC_three_1452									82.47 313	97.09 19	97.07 72	92.72 1	98.04 202	92.70 81	99.02 12	98.86 16

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

eth-test2					0.00 564

eth-test					0.00 564

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

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

IU-MVS					98.77 8	86.00 55		96.84 83	81.26 351	97.26 13				95.50 37	99.13 3	99.03 10

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_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

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

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

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

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

GSMVS																96.12 234

test_part2					98.55 15	87.22 20				96.40 31

sam_mvs1												71.70 291				96.12 234

sam_mvs												70.60 305

MTGPA								96.97 66

test_post1							88.00 445				9.81 554	69.31 330	95.53 407	76.65 373

test_post											10.29 553	70.57 309	95.91 392

patchmatchnet-post											83.76 467	71.53 292	96.48 360

MTMP							96.16 60	60.64 517

gm-plane-assit					89.60 432	68.00 468			77.28 408		88.99 411		97.57 250	79.44 343

test9_res														91.91 111	98.71 36	98.07 84

TEST9					97.53 68	86.49 39	94.07 232	96.78 91	81.61 343	92.77 102	96.20 110	87.71 33	99.12 64

test_8					97.49 70	86.30 47	94.02 238	96.76 94	81.86 334	92.70 106	96.20 110	87.63 34	99.02 74

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_prior2							94.12 224		87.67 160	92.63 110	96.39 105	86.62 46		91.50 121	98.67 44

旧先验2							93.36 278		71.25 470	94.37 62			97.13 307	86.74 206

新几何2							93.11 293

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

无先验							93.28 286	96.26 141	73.95 450				99.05 68	80.56 317		96.59 213

原ACMM2							92.94 304

test222					96.55 96	81.70 205	92.22 339	95.01 264	68.36 480	90.20 182	96.14 120	80.26 148			97.80 92	96.05 241

testdata2													98.75 117	78.30 357

segment_acmp												87.16 41

testdata1							92.15 341		87.94 144

plane_prior7					94.70 203	82.74 166

plane_prior6					94.52 219	82.75 164						74.23 251

plane_prior5								96.22 147					98.12 181	88.15 181	89.99 287	94.63 296

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						89.88 292

n2								0.00 566

nn								0.00 566

door-mid								85.49 480

test11								96.57 113

door								85.33 482

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			94.51 306

HQP3-MVS								96.04 174							89.77 296

HQP2-MVS												73.83 262

NP-MVS					94.37 233	82.42 181					93.98 247

MDTV_nov1_ep13_2view						55.91 508	87.62 453		73.32 456	84.59 321		70.33 312		74.65 396		95.50 262

MDTV_nov1_ep13			83.56 361		91.69 363	69.93 460	87.75 450	91.54 403	78.60 389	84.86 315	88.90 413	69.54 324	96.03 383	70.25 429	88.93 309

ACMMP++_ref															87.47 332

ACMMP++															88.01 324

Test By Simon												80.02 150

High-resolution multi-view benchmark