High-res multi-view results

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

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

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

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

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

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

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

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 65	95.06 1	94.23 6	78.38 38	92.78 4	95.74 8	82.45 3	97.49 4	89.42 19	96.68 2	94.95 13

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 73	93.57 8	94.06 14	77.24 63	93.10 1	95.72 10	82.99 1	97.44 7	89.07 25	96.63 4	94.88 17

MED-MVS		89.75 3	90.37 3	87.89 24	94.57 17	71.43 61	93.28 12	94.36 3	77.30 60	92.25 9	95.87 3	81.59 7	97.39 11	88.15 39	95.96 19	94.85 22

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 65	93.49 10	92.73 70	77.33 58	92.12 11	95.78 6	80.98 10	97.40 9	89.08 22	96.41 12	93.33 125
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
DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 12	75.90 110	92.29 7	95.66 12	81.67 6	97.38 13	87.44 48	96.34 15	93.95 87
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 10	80.27 10	91.35 16	94.16 54	78.35 14	96.77 28	89.59 17	94.22 65	94.67 40
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
APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 22	71.69 55	93.83 4	93.96 17	75.70 117	91.06 19	96.03 1	76.84 18	97.03 20	89.09 21	95.65 30	94.47 58
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ME-MVS		88.98 11	89.39 8	87.75 30	94.54 20	71.43 61	91.61 49	94.25 5	76.30 102	90.62 21	95.03 22	78.06 15	97.07 19	88.15 39	95.96 19	94.75 33

MM		89.16 7	89.23 9	88.97 4	90.79 103	73.65 10	92.66 28	91.17 152	86.57 1	87.39 58	94.97 25	71.70 63	97.68 1	92.19 1	95.63 31	95.57 1

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 149	92.29 7	95.97 2	74.28 34	97.24 15	88.58 33	96.91 1	94.87 19
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
TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 17	71.25 65	93.28 12	93.91 19	77.30 60	91.13 18	95.87 3	77.62 16	96.95 22	86.12 57	93.07 75	94.85 22

HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 65	80.26 11	87.78 49	94.27 47	75.89 23	96.81 27	87.45 47	96.44 9	93.05 145

CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 52	80.90 7	88.06 44	94.06 59	76.43 20	96.84 25	88.48 36	95.99 18	94.34 65

SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 79	72.96 25	93.73 5	93.67 25	80.19 12	88.10 43	94.80 27	73.76 38	97.11 17	87.51 46	95.82 24	94.90 16
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 15	88.74 15	87.64 39	92.78 71	71.95 52	92.40 29	94.74 2	75.71 115	89.16 29	95.10 20	75.65 25	96.19 52	87.07 49	96.01 17	94.79 26

lecture		88.09 17	88.59 16	86.58 63	93.26 56	69.77 97	93.70 6	94.16 8	77.13 68	89.76 26	95.52 16	72.26 54	96.27 49	86.87 50	94.65 51	93.70 103

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 60	92.24 78	69.03 111	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 13	96.58 40	85.66 58	95.72 27	94.58 49

SD-MVS		88.06 18	88.50 18	86.71 61	92.60 76	72.71 29	91.81 46	93.19 41	77.87 42	90.32 23	94.00 63	74.83 27	93.78 160	87.63 45	94.27 64	93.65 108
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
9.14			88.26 19		92.84 70		91.52 56	94.75 1	73.93 172	88.57 36	94.67 30	75.57 26	95.79 64	86.77 51	95.76 26

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 88	74.62 153	88.90 33	93.85 71	75.75 24	96.00 60	87.80 43	94.63 53	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 103	88.14 42	95.09 21	71.06 73	96.67 33	87.67 44	96.37 14	94.09 79

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 66	81.50 5	85.79 73	93.47 81	73.02 46	97.00 21	84.90 64	94.94 43	94.10 78

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 82	89.48 139	67.88 154	88.59 147	89.05 237	80.19 12	90.70 20	95.40 17	74.56 29	93.92 153	91.54 2	92.07 92	95.31 5

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 78	85.24 78	94.32 44	71.76 61	96.93 23	85.53 61	95.79 25	94.32 67

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 51	89.80 90	93.50 30	75.17 136	86.34 69	95.29 19	70.86 75	96.00 60	88.78 31	96.04 16	94.58 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 76	77.57 49	83.84 110	94.40 41	72.24 55	96.28 48	85.65 59	95.30 38	93.62 111
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 140	88.96 30	95.54 14	71.20 71	96.54 41	86.28 54	93.49 70	93.06 143

our_new_method		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 140	88.96 30	95.54 14	71.20 71	96.54 41	86.28 54	93.49 70	93.06 143

MGCNet		87.69 24	87.55 29	88.12 13	89.45 140	71.76 54	91.47 57	89.54 209	82.14 3	86.65 67	94.28 46	68.28 121	97.46 6	90.81 6	95.31 37	95.15 8

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 36	72.39 41	91.86 45	92.83 66	73.01 201	88.58 35	94.52 32	73.36 39	96.49 43	84.26 75	95.01 40	92.70 159
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 39	76.78 80	84.91 83	94.44 39	70.78 76	96.61 37	84.53 72	94.89 45	93.66 104

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 210	87.08 263	65.21 227	89.09 123	90.21 186	79.67 19	89.98 24	95.02 24	73.17 43	91.71 272	91.30 3	91.60 100	92.34 176

reproduce_model		87.28 35	87.39 33	86.95 55	93.10 62	71.24 70	91.60 50	93.19 41	74.69 150	88.80 34	95.61 13	70.29 82	96.44 44	86.20 56	93.08 74	93.16 135

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 76	84.68 87	93.99 65	70.67 78	96.82 26	84.18 79	95.01 40	93.90 90

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 199	84.86 86	92.89 96	76.22 21	96.33 46	84.89 66	95.13 39	94.40 61

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 40	76.78 80	84.66 90	94.52 32	68.81 112	96.65 35	84.53 72	94.90 44	94.00 84

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 46	76.73 83	84.45 95	94.52 32	69.09 106	96.70 31	84.37 74	94.83 48	94.03 82

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 123	87.76 223	65.62 214	89.20 114	92.21 103	79.94 17	89.74 27	94.86 26	68.63 115	94.20 138	90.83 5	91.39 105	94.38 62

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 230	92.02 112	79.45 22	85.88 71	94.80 27	68.07 123	96.21 51	86.69 52	95.34 35	93.23 128

BridgeMVS		86.78 42	86.99 40	86.15 71	91.24 91	67.61 163	90.51 70	92.90 62	77.26 62	87.44 57	91.63 137	71.27 70	96.06 55	85.62 60	95.01 40	94.78 27

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 107	83.81 111	93.95 68	69.77 93	96.01 59	85.15 62	94.66 50	94.32 67
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CS-MVS		86.69 44	86.95 42	85.90 79	90.76 104	67.57 165	92.83 22	93.30 38	79.67 19	84.57 94	92.27 108	71.47 66	95.02 101	84.24 77	93.46 72	95.13 9

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 69	76.62 86	83.68 113	94.46 36	67.93 124	95.95 63	84.20 78	94.39 60	93.23 128

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 26	83.67 114	94.17 53	67.45 129	96.60 38	83.06 87	94.50 56	94.07 80

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 92	72.32 45	90.31 79	93.94 18	77.12 69	82.82 136	94.23 50	72.13 57	97.09 18	84.83 67	95.37 34	93.65 108
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 97	87.33 248	67.30 176	89.50 101	90.98 157	76.25 104	90.56 22	94.75 29	68.38 118	94.24 137	90.80 7	92.32 89	94.19 73

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 175	87.78 220	66.09 198	89.96 86	90.80 165	77.37 57	86.72 66	94.20 52	72.51 52	92.78 227	89.08 22	92.33 87	93.13 139

SR-MVS		86.73 43	86.67 48	86.91 56	94.11 41	72.11 49	92.37 33	92.56 81	74.50 154	86.84 65	94.65 31	67.31 131	95.77 65	84.80 68	92.85 78	92.84 157

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 193	87.12 262	66.01 201	88.56 149	89.43 213	75.59 119	89.32 28	94.32 44	72.89 47	91.21 300	90.11 11	92.33 87	93.16 135

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 47	76.62 86	84.22 101	93.36 85	71.44 67	96.76 29	80.82 113	95.33 36	94.16 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SPE-MVS-test		86.29 54	86.48 51	85.71 81	91.02 96	67.21 182	92.36 34	93.78 23	78.97 33	83.51 121	91.20 155	70.65 79	95.15 92	81.96 102	94.89 45	94.77 28

EC-MVSNet		86.01 58	86.38 52	84.91 115	89.31 149	66.27 196	92.32 35	93.63 26	79.37 23	84.17 103	91.88 124	69.04 110	95.43 78	83.93 81	93.77 68	93.01 148

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 100	87.20 254	68.54 131	89.57 99	90.44 175	75.31 128	87.49 55	94.39 42	72.86 48	92.72 228	89.04 27	90.56 120	94.16 74

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 101	76.87 77	82.81 137	94.25 49	66.44 143	96.24 50	82.88 92	94.28 63	93.38 121

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 47	75.53 120	83.86 109	94.42 40	67.87 126	96.64 36	82.70 98	94.57 55	93.66 104

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 188	87.32 250	65.13 230	88.86 130	91.63 136	75.41 124	88.23 41	93.45 82	68.56 116	92.47 239	89.52 18	92.78 79	93.20 133

train_agg		86.43 49	86.20 56	87.13 50	93.26 56	72.96 25	88.75 138	91.89 120	68.69 308	85.00 81	93.10 89	74.43 31	95.41 81	84.97 63	95.71 28	93.02 147

CSCG		86.41 51	86.19 58	87.07 51	92.91 67	72.48 37	90.81 66	93.56 29	73.95 170	83.16 128	91.07 160	75.94 22	95.19 90	79.94 124	94.38 61	93.55 116

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 100	70.96 75	92.27 37	94.07 13	72.45 207	85.22 79	91.90 123	69.47 96	96.42 45	83.28 86	95.94 22	94.35 64

dcpmvs_2		85.63 70	86.15 60	84.06 167	91.71 85	64.94 240	86.47 234	91.87 122	73.63 179	86.60 68	93.02 94	76.57 19	91.87 266	83.36 84	92.15 90	95.35 3

CANet		86.45 48	86.10 61	87.51 42	90.09 116	70.94 77	89.70 94	92.59 80	81.78 4	81.32 160	91.43 147	70.34 80	97.23 16	84.26 75	93.36 73	94.37 63

casdiffmvs_mvg		85.99 59	86.09 62	85.70 82	87.65 231	67.22 181	88.69 143	93.04 47	79.64 21	85.33 77	92.54 105	73.30 40	94.50 126	83.49 83	91.14 110	95.37 2
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_fmvsmconf_n		85.92 62	86.04 63	85.57 88	85.03 318	69.51 101	89.62 98	90.58 170	73.42 187	87.75 51	94.02 61	72.85 49	93.24 197	90.37 8	90.75 117	93.96 85

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 74	91.09 93	67.64 162	89.63 97	92.65 76	72.89 204	84.64 91	91.71 132	71.85 59	96.03 56	84.77 69	94.45 59	94.49 57

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 106	79.31 24	84.39 97	92.18 114	64.64 165	95.53 72	80.70 116	94.65 51	94.56 53

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 68	92.69 73	69.53 100	91.93 42	92.99 55	73.54 183	85.94 70	94.51 35	65.80 155	95.61 68	83.04 89	92.51 83	93.53 118

sasdasda		85.91 63	85.87 67	86.04 75	89.84 126	69.44 106	90.45 76	93.00 52	76.70 84	88.01 46	91.23 151	73.28 41	93.91 154	81.50 105	88.80 152	94.77 28

canonicalmvs		85.91 63	85.87 67	86.04 75	89.84 126	69.44 106	90.45 76	93.00 52	76.70 84	88.01 46	91.23 151	73.28 41	93.91 154	81.50 105	88.80 152	94.77 28

MSLP-MVS++		85.43 75	85.76 69	84.45 135	91.93 82	70.24 86	90.71 67	92.86 64	77.46 55	84.22 101	92.81 100	67.16 133	92.94 218	80.36 119	94.35 62	90.16 261

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 147	86.70 274	65.83 208	88.77 136	89.78 198	75.46 123	88.35 37	93.73 74	69.19 105	93.06 213	91.30 3	88.44 161	94.02 83

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 89	82.99 372	69.39 108	89.65 95	90.29 184	73.31 191	87.77 50	94.15 55	71.72 62	93.23 198	90.31 9	90.67 119	93.89 91

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 67	93.06 64	70.63 83	91.88 43	92.27 94	73.53 184	85.69 74	94.45 37	65.00 163	95.56 69	82.75 94	91.87 96	92.50 169

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 154	86.26 283	67.40 172	89.18 115	89.31 222	72.50 206	88.31 38	93.86 70	69.66 94	91.96 260	89.81 13	91.05 111	93.38 121

MGCFI-Net		85.06 85	85.51 74	83.70 186	89.42 141	63.01 293	89.43 104	92.62 79	76.43 93	87.53 54	91.34 149	72.82 50	93.42 190	81.28 108	88.74 155	94.66 43

RE-MVS-def			85.48 75		93.06 64	70.63 83	91.88 43	92.27 94	73.53 184	85.69 74	94.45 37	63.87 171		82.75 94	91.87 96	92.50 169

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 45	76.78 80	80.73 174	93.82 72	64.33 167	96.29 47	82.67 99	90.69 118	93.23 128
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
test_fmvsm_n_1920		85.29 80	85.34 77	85.13 103	86.12 289	69.93 93	88.65 145	90.78 166	69.97 272	88.27 39	93.98 66	71.39 68	91.54 282	88.49 35	90.45 122	93.91 88

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 57	91.34 89	72.50 36	89.07 124	87.28 292	76.41 94	85.80 72	90.22 188	74.15 36	95.37 86	81.82 103	91.88 95	92.65 163

alignmvs		85.48 73	85.32 79	85.96 78	89.51 136	69.47 103	89.74 92	92.47 82	76.17 105	87.73 53	91.46 146	70.32 81	93.78 160	81.51 104	88.95 149	94.63 46

DELS-MVS		85.41 76	85.30 80	85.77 80	88.49 184	67.93 153	85.52 270	93.44 32	78.70 34	83.63 116	89.03 221	74.57 28	95.71 67	80.26 121	94.04 66	93.66 104
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
CDPH-MVS		85.76 68	85.29 81	87.17 49	93.49 51	71.08 71	88.58 148	92.42 86	68.32 315	84.61 92	93.48 79	72.32 53	96.15 54	79.00 140	95.43 33	94.28 70

casdiffmvs		85.11 83	85.14 82	85.01 108	87.20 254	65.77 212	87.75 182	92.83 66	77.84 43	84.36 100	92.38 107	72.15 56	93.93 152	81.27 109	90.48 121	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		84.93 86	84.98 83	84.80 120	87.30 252	65.39 220	87.30 202	92.88 63	77.62 47	84.04 106	92.26 109	71.81 60	93.96 146	81.31 107	90.30 124	95.03 11

UA-Net		85.08 84	84.96 84	85.45 90	92.07 80	68.07 146	89.78 91	90.86 163	82.48 2	84.60 93	93.20 88	69.35 98	95.22 89	71.39 235	90.88 116	93.07 142

HPM-MVS_fast		85.35 79	84.95 85	86.57 64	93.69 46	70.58 85	92.15 40	91.62 137	73.89 173	82.67 140	94.09 57	62.60 190	95.54 71	80.93 111	92.93 77	93.57 114

SymmetryMVS		85.38 78	84.81 86	87.07 51	91.47 88	72.47 38	91.65 47	88.06 271	79.31 24	84.39 97	92.18 114	64.64 165	95.53 72	80.70 116	90.91 115	93.21 131

MVS_111021_HR		85.14 82	84.75 87	86.32 66	91.65 86	72.70 30	85.98 252	90.33 181	76.11 106	82.08 147	91.61 140	71.36 69	94.17 141	81.02 110	92.58 82	92.08 192

ETV-MVS		84.90 88	84.67 88	85.59 87	89.39 144	68.66 128	88.74 140	92.64 78	79.97 16	84.10 104	85.71 317	69.32 99	95.38 83	80.82 113	91.37 106	92.72 158

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 151	85.42 305	68.81 117	88.49 151	87.26 297	68.08 317	88.03 45	93.49 78	72.04 58	91.77 268	88.90 29	89.14 148	92.24 183

patch_mono-2		83.65 114	84.54 89	80.99 278	90.06 121	65.83 208	84.21 306	88.74 256	71.60 225	85.01 80	92.44 106	74.51 30	83.50 419	82.15 101	92.15 90	93.64 110

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 94	80.25 414	69.03 111	89.47 102	89.65 205	73.24 195	86.98 63	94.27 47	66.62 139	93.23 198	90.26 10	89.95 132	93.78 100

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 94	73.49 16	93.18 16	93.78 23	80.79 8	76.66 257	93.37 84	60.40 238	96.75 30	77.20 162	93.73 69	95.29 6

DPM-MVS		84.93 86	84.29 93	86.84 57	90.20 114	73.04 23	87.12 206	93.04 47	69.80 276	82.85 135	91.22 154	73.06 45	96.02 58	76.72 174	94.63 53	91.46 213

fmvsm_l_conf0.5_n_a		84.13 97	84.16 94	84.06 167	85.38 306	68.40 134	88.34 159	86.85 309	67.48 324	87.48 56	93.40 83	70.89 74	91.61 273	88.38 37	89.22 145	92.16 190

E5new		84.22 92	84.12 95	84.51 130	87.60 233	65.36 222	87.45 192	92.31 90	76.51 89	83.53 117	92.26 109	69.25 103	93.50 180	79.88 125	88.26 163	94.69 35

E6new		84.22 92	84.12 95	84.52 128	87.60 233	65.36 222	87.45 192	92.30 92	76.51 89	83.53 117	92.26 109	69.26 101	93.49 182	79.88 125	88.26 163	94.69 35

E6		84.22 92	84.12 95	84.52 128	87.60 233	65.36 222	87.45 192	92.30 92	76.51 89	83.53 117	92.26 109	69.26 101	93.49 182	79.88 125	88.26 163	94.69 35

E5		84.22 92	84.12 95	84.51 130	87.60 233	65.36 222	87.45 192	92.31 90	76.51 89	83.53 117	92.26 109	69.25 103	93.50 180	79.88 125	88.26 163	94.69 35

fmvsm_s_conf0.5_n_2		84.04 99	84.11 99	83.81 184	86.17 287	65.00 235	86.96 212	87.28 292	74.35 159	88.25 40	94.23 50	61.82 206	92.60 231	89.85 12	88.09 171	93.84 94

fmvsm_s_conf0.5_n_7		83.34 126	84.03 100	81.28 269	85.73 296	65.13 230	85.40 271	89.90 196	74.96 142	82.13 146	93.89 69	66.65 138	87.92 372	86.56 53	91.05 111	90.80 232

E4		84.10 98	83.99 101	84.45 135	87.58 241	64.99 236	86.54 232	92.25 97	76.38 98	83.37 122	92.09 120	69.88 91	93.58 169	79.78 130	88.03 174	94.77 28

E2		84.00 101	83.87 102	84.39 138	87.70 228	64.95 237	86.40 239	92.23 98	75.85 111	83.21 124	91.78 128	70.09 86	93.55 174	79.52 133	88.05 172	94.66 43

E3		84.00 101	83.87 102	84.39 138	87.70 228	64.95 237	86.40 239	92.23 98	75.85 111	83.21 124	91.78 128	70.09 86	93.55 174	79.52 133	88.05 172	94.66 43

test_fmvsmvis_n_1920		84.02 100	83.87 102	84.49 134	84.12 336	69.37 109	88.15 168	87.96 274	70.01 270	83.95 108	93.23 87	68.80 113	91.51 285	88.61 32	89.96 131	92.57 164

EI-MVSNet-Vis-set		84.19 96	83.81 105	85.31 95	88.18 196	67.85 155	87.66 184	89.73 203	80.05 15	82.95 131	89.59 206	70.74 77	94.82 110	80.66 118	84.72 234	93.28 127

fmvsm_s_conf0.1_n_2		83.80 107	83.79 106	83.83 182	85.62 299	64.94 240	87.03 209	86.62 316	74.32 160	87.97 48	94.33 43	60.67 230	92.60 231	89.72 14	87.79 178	93.96 85

viewcassd2359sk11		83.89 104	83.74 107	84.34 143	87.76 223	64.91 243	86.30 243	92.22 101	75.47 122	83.04 130	91.52 142	70.15 84	93.53 177	79.26 135	87.96 175	94.57 51

BP-MVS1		84.32 91	83.71 108	86.17 69	87.84 215	67.85 155	89.38 109	89.64 206	77.73 45	83.98 107	92.12 119	56.89 268	95.43 78	84.03 80	91.75 99	95.24 7

fmvsm_s_conf0.5_n		83.80 107	83.71 108	84.07 164	86.69 275	67.31 175	89.46 103	83.07 370	71.09 237	86.96 64	93.70 75	69.02 111	91.47 288	88.79 30	84.62 236	93.44 120

viewmacassd2359aftdt		83.76 110	83.66 110	84.07 164	86.59 278	64.56 249	86.88 217	91.82 125	75.72 114	83.34 123	92.15 118	68.24 122	92.88 221	79.05 136	89.15 147	94.77 28

balanced_ft_v1		83.98 103	83.64 111	85.03 106	89.76 129	65.86 207	88.31 161	91.71 132	74.41 158	80.41 180	90.82 169	62.90 188	94.90 105	83.04 89	91.37 106	94.32 67

E3new		83.78 109	83.60 112	84.31 145	87.76 223	64.89 244	86.24 246	92.20 104	75.15 137	82.87 133	91.23 151	70.11 85	93.52 179	79.05 136	87.79 178	94.51 56

viewmanbaseed2359cas		83.66 113	83.55 113	84.00 175	86.81 270	64.53 250	86.65 227	91.75 130	74.89 144	83.15 129	91.68 133	68.74 114	92.83 225	79.02 138	89.24 144	94.63 46

nrg030		83.88 105	83.53 114	84.96 110	86.77 272	69.28 110	90.46 75	92.67 73	74.79 148	82.95 131	91.33 150	72.70 51	93.09 211	80.79 115	79.28 318	92.50 169

MG-MVS		83.41 123	83.45 115	83.28 200	92.74 72	62.28 310	88.17 166	89.50 211	75.22 130	81.49 158	92.74 104	66.75 137	95.11 95	72.85 217	91.58 102	92.45 173

fmvsm_s_conf0.5_n_a		83.63 116	83.41 116	84.28 149	86.14 288	68.12 144	89.43 104	82.87 375	70.27 265	87.27 60	93.80 73	69.09 106	91.58 275	88.21 38	83.65 257	93.14 138

fmvsm_s_conf0.1_n		83.56 119	83.38 117	84.10 158	84.86 320	67.28 177	89.40 108	83.01 371	70.67 249	87.08 61	93.96 67	68.38 118	91.45 289	88.56 34	84.50 237	93.56 115

EI-MVSNet-UG-set		83.81 106	83.38 117	85.09 105	87.87 213	67.53 167	87.44 197	89.66 204	79.74 18	82.23 144	89.41 215	70.24 83	94.74 116	79.95 123	83.92 249	92.99 150

CPTT-MVS		83.73 111	83.33 119	84.92 114	93.28 53	70.86 79	92.09 41	90.38 177	68.75 307	79.57 190	92.83 98	60.60 234	93.04 216	80.92 112	91.56 103	90.86 231

HQP_MVS		83.64 115	83.14 120	85.14 100	90.08 117	68.71 124	91.25 60	92.44 83	79.12 28	78.92 202	91.00 164	60.42 236	95.38 83	78.71 144	86.32 205	91.33 214

Effi-MVS+		83.62 117	83.08 121	85.24 97	88.38 190	67.45 169	88.89 129	89.15 233	75.50 121	82.27 143	88.28 246	69.61 95	94.45 129	77.81 154	87.84 177	93.84 94

MVS_Test		83.15 131	83.06 122	83.41 197	86.86 267	63.21 289	86.11 250	92.00 114	74.31 161	82.87 133	89.44 214	70.03 88	93.21 200	77.39 161	88.50 160	93.81 96

casdiffseed414692147		83.62 117	83.02 123	85.40 92	87.31 251	67.50 168	88.70 142	91.72 131	76.97 73	82.77 138	91.72 131	66.85 136	93.71 167	73.06 215	88.12 170	94.98 12

EPP-MVSNet		83.40 124	83.02 123	84.57 126	90.13 115	64.47 255	92.32 35	90.73 167	74.45 157	79.35 196	91.10 158	69.05 109	95.12 93	72.78 218	87.22 189	94.13 76

fmvsm_s_conf0.1_n_a		83.32 128	82.99 125	84.28 149	83.79 344	68.07 146	89.34 111	82.85 376	69.80 276	87.36 59	94.06 59	68.34 120	91.56 278	87.95 42	83.46 263	93.21 131

OPM-MVS		83.50 121	82.95 126	85.14 100	88.79 174	70.95 76	89.13 121	91.52 141	77.55 52	80.96 168	91.75 130	60.71 228	94.50 126	79.67 132	86.51 203	89.97 277
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EPNet		83.72 112	82.92 127	86.14 73	84.22 334	69.48 102	91.05 64	85.27 334	81.30 6	76.83 252	91.65 135	66.09 150	95.56 69	76.00 181	93.85 67	93.38 121
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IS-MVSNet		83.15 131	82.81 128	84.18 156	89.94 124	63.30 287	91.59 51	88.46 264	79.04 30	79.49 191	92.16 116	65.10 160	94.28 132	67.71 273	91.86 98	94.95 13

EIA-MVS		83.31 129	82.80 129	84.82 118	89.59 132	65.59 215	88.21 164	92.68 72	74.66 152	78.96 200	86.42 304	69.06 108	95.26 88	75.54 188	90.09 128	93.62 111

Vis-MVSNet		83.46 122	82.80 129	85.43 91	90.25 113	68.74 122	90.30 80	90.13 189	76.33 101	80.87 171	92.89 96	61.00 225	94.20 138	72.45 227	90.97 113	93.35 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
viewdifsd2359ckpt07		82.83 139	82.78 131	82.99 217	86.51 280	62.58 301	85.09 279	90.83 164	75.22 130	82.28 142	91.63 137	69.43 97	92.03 256	77.71 156	86.32 205	94.34 65

GDP-MVS		83.52 120	82.64 132	86.16 70	88.14 199	68.45 133	89.13 121	92.69 71	72.82 205	83.71 112	91.86 126	55.69 277	95.35 87	80.03 122	89.74 136	94.69 35

KinetiMVS		83.31 129	82.61 133	85.39 93	87.08 263	67.56 166	88.06 170	91.65 135	77.80 44	82.21 145	91.79 127	57.27 263	94.07 144	77.77 155	89.89 134	94.56 53

viewdifsd2359ckpt09		83.34 126	82.55 134	85.70 82	87.64 232	67.72 160	88.43 152	91.68 134	71.91 219	81.65 156	90.68 172	67.10 134	94.75 115	76.17 177	87.70 181	94.62 48

FIs		82.07 150	82.42 135	81.04 277	88.80 173	58.34 367	88.26 163	93.49 31	76.93 75	78.47 214	91.04 161	69.92 90	92.34 247	69.87 254	84.97 230	92.44 174

VNet		82.21 147	82.41 136	81.62 258	90.82 101	60.93 335	84.47 295	89.78 198	76.36 100	84.07 105	91.88 124	64.71 164	90.26 329	70.68 242	88.89 150	93.66 104

PAPM_NR		83.02 135	82.41 136	84.82 118	92.47 77	66.37 194	87.93 176	91.80 126	73.82 174	77.32 240	90.66 173	67.90 125	94.90 105	70.37 245	89.48 141	93.19 134

VDD-MVS		83.01 136	82.36 138	84.96 110	91.02 96	66.40 193	88.91 128	88.11 267	77.57 49	84.39 97	93.29 86	52.19 311	93.91 154	77.05 165	88.70 156	94.57 51

3Dnovator	76.31 5	83.38 125	82.31 139	86.59 62	87.94 210	72.94 28	90.64 68	92.14 111	77.21 65	75.47 283	92.83 98	58.56 250	94.72 117	73.24 213	92.71 81	92.13 191

viewdifsd2359ckpt13		82.91 137	82.29 140	84.77 121	86.96 266	66.90 189	87.47 189	91.62 137	72.19 212	81.68 155	90.71 171	66.92 135	93.28 193	75.90 182	87.15 191	94.12 77

diffmvs_AUTHOR		82.38 145	82.27 141	82.73 236	83.26 358	63.80 269	83.89 313	89.76 200	73.35 190	82.37 141	90.84 167	66.25 146	90.79 319	82.77 93	87.93 176	93.59 113

h-mvs33		83.15 131	82.19 142	86.02 77	90.56 106	70.85 80	88.15 168	89.16 232	76.02 108	84.67 88	91.39 148	61.54 211	95.50 74	82.71 96	75.48 369	91.72 203

MVS_111021_LR		82.61 142	82.11 143	84.11 157	88.82 168	71.58 58	85.15 276	86.16 324	74.69 150	80.47 179	91.04 161	62.29 197	90.55 325	80.33 120	90.08 129	90.20 260

RRT-MVS		82.60 144	82.10 144	84.10 158	87.98 209	62.94 298	87.45 192	91.27 148	77.42 56	79.85 186	90.28 184	56.62 271	94.70 119	79.87 129	88.15 169	94.67 40

DP-MVS Recon		83.11 134	82.09 145	86.15 71	94.44 23	70.92 78	88.79 135	92.20 104	70.53 254	79.17 198	91.03 163	64.12 169	96.03 56	68.39 270	90.14 127	91.50 209

MVSFormer		82.85 138	82.05 146	85.24 97	87.35 243	70.21 87	90.50 72	90.38 177	68.55 310	81.32 160	89.47 209	61.68 208	93.46 187	78.98 141	90.26 125	92.05 193

FC-MVSNet-test		81.52 166	82.02 147	80.03 303	88.42 189	55.97 407	87.95 174	93.42 34	77.10 70	77.38 238	90.98 166	69.96 89	91.79 267	68.46 269	84.50 237	92.33 177

HQP-MVS		82.61 142	82.02 147	84.37 140	89.33 146	66.98 185	89.17 116	92.19 106	76.41 94	77.23 243	90.23 187	60.17 239	95.11 95	77.47 159	85.99 214	91.03 224

OMC-MVS		82.69 140	81.97 149	84.85 117	88.75 176	67.42 170	87.98 172	90.87 162	74.92 143	79.72 188	91.65 135	62.19 200	93.96 146	75.26 192	86.42 204	93.16 135

diffmvs		82.10 148	81.88 150	82.76 234	83.00 368	63.78 271	83.68 318	89.76 200	72.94 202	82.02 148	89.85 193	65.96 154	90.79 319	82.38 100	87.30 188	93.71 102
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		82.62 141	81.83 151	84.96 110	90.80 102	69.76 98	88.74 140	91.70 133	69.39 285	78.96 200	88.46 241	65.47 157	94.87 109	74.42 199	88.57 157	90.24 259

CLD-MVS		82.31 146	81.65 152	84.29 148	88.47 185	67.73 159	85.81 260	92.35 88	75.78 113	78.33 217	86.58 299	64.01 170	94.35 130	76.05 180	87.48 185	90.79 233
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
UniMVSNet_NR-MVSNet		81.88 154	81.54 153	82.92 221	88.46 186	63.46 283	87.13 205	92.37 87	80.19 12	78.38 215	89.14 217	71.66 65	93.05 214	70.05 250	76.46 352	92.25 181

PS-MVSNAJss		82.07 150	81.31 154	84.34 143	86.51 280	67.27 178	89.27 112	91.51 142	71.75 220	79.37 195	90.22 188	63.15 181	94.27 133	77.69 157	82.36 278	91.49 210

LPG-MVS_test		82.08 149	81.27 155	84.50 132	89.23 154	68.76 120	90.22 81	91.94 118	75.37 126	76.64 258	91.51 143	54.29 290	94.91 103	78.44 146	83.78 250	89.83 282

LFMVS		81.82 156	81.23 156	83.57 191	91.89 83	63.43 285	89.84 87	81.85 389	77.04 72	83.21 124	93.10 89	52.26 310	93.43 189	71.98 230	89.95 132	93.85 92

API-MVS		81.99 152	81.23 156	84.26 153	90.94 98	70.18 92	91.10 63	89.32 221	71.51 227	78.66 207	88.28 246	65.26 158	95.10 98	64.74 300	91.23 109	87.51 354

UniMVSNet (Re)		81.60 162	81.11 158	83.09 210	88.38 190	64.41 257	87.60 185	93.02 51	78.42 37	78.56 210	88.16 250	69.78 92	93.26 196	69.58 257	76.49 351	91.60 204

xiu_mvs_v2_base		81.69 159	81.05 159	83.60 188	89.15 157	68.03 148	84.46 297	90.02 191	70.67 249	81.30 163	86.53 302	63.17 180	94.19 140	75.60 187	88.54 158	88.57 327

PS-MVSNAJ		81.69 159	81.02 160	83.70 186	89.51 136	68.21 143	84.28 305	90.09 190	70.79 246	81.26 164	85.62 322	63.15 181	94.29 131	75.62 186	88.87 151	88.59 326

GeoE		81.71 158	81.01 161	83.80 185	89.51 136	64.45 256	88.97 126	88.73 257	71.27 233	78.63 208	89.76 199	66.32 145	93.20 203	69.89 253	86.02 213	93.74 101

hse-mvs2		81.72 157	80.94 162	84.07 164	88.72 177	67.68 161	85.87 256	87.26 297	76.02 108	84.67 88	88.22 249	61.54 211	93.48 185	82.71 96	73.44 397	91.06 222

PAPR		81.66 161	80.89 163	83.99 177	90.27 112	64.00 263	86.76 224	91.77 129	68.84 306	77.13 250	89.50 207	67.63 127	94.88 108	67.55 275	88.52 159	93.09 141

SSM_0404		81.91 153	80.84 164	85.13 103	89.24 153	68.26 138	87.84 181	89.25 227	71.06 239	80.62 175	90.39 181	59.57 241	94.65 121	72.45 227	87.19 190	92.47 172

MAR-MVS		81.84 155	80.70 165	85.27 96	91.32 90	71.53 59	89.82 88	90.92 159	69.77 278	78.50 211	86.21 308	62.36 196	94.52 125	65.36 294	92.05 93	89.77 285
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
VDDNet		81.52 166	80.67 166	84.05 170	90.44 109	64.13 262	89.73 93	85.91 327	71.11 236	83.18 127	93.48 79	50.54 342	93.49 182	73.40 210	88.25 167	94.54 55

guyue		81.13 173	80.64 167	82.60 239	86.52 279	63.92 267	86.69 226	87.73 282	73.97 169	80.83 173	89.69 200	56.70 269	91.33 294	78.26 153	85.40 227	92.54 166

ACMP	74.13 6	81.51 168	80.57 168	84.36 141	89.42 141	68.69 127	89.97 85	91.50 145	74.46 156	75.04 305	90.41 180	53.82 296	94.54 123	77.56 158	82.91 270	89.86 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VPA-MVSNet		80.60 192	80.55 169	80.76 284	88.07 204	60.80 338	86.86 218	91.58 140	75.67 118	80.24 182	89.45 213	63.34 174	90.25 330	70.51 244	79.22 319	91.23 217

DU-MVS		81.12 174	80.52 170	82.90 222	87.80 217	63.46 283	87.02 210	91.87 122	79.01 31	78.38 215	89.07 219	65.02 161	93.05 214	70.05 250	76.46 352	92.20 184

SSM_0407		81.58 163	80.48 171	84.87 116	88.81 169	67.96 150	87.37 198	89.25 227	71.06 239	79.48 192	90.39 181	59.57 241	94.48 128	72.45 227	85.93 216	92.18 186

test_yl		81.17 171	80.47 172	83.24 203	89.13 158	63.62 272	86.21 247	89.95 194	72.43 210	81.78 153	89.61 204	57.50 260	93.58 169	70.75 240	86.90 195	92.52 167

DCV-MVSNet		81.17 171	80.47 172	83.24 203	89.13 158	63.62 272	86.21 247	89.95 194	72.43 210	81.78 153	89.61 204	57.50 260	93.58 169	70.75 240	86.90 195	92.52 167

PVSNet_Blended		80.98 175	80.34 174	82.90 222	88.85 165	65.40 218	84.43 300	92.00 114	67.62 321	78.11 222	85.05 338	66.02 152	94.27 133	71.52 232	89.50 140	89.01 307

TranMVSNet+NR-MVSNet		80.84 178	80.31 175	82.42 242	87.85 214	62.33 308	87.74 183	91.33 147	80.55 9	77.99 226	89.86 192	65.23 159	92.62 229	67.05 282	75.24 379	92.30 179

jason		81.39 169	80.29 176	84.70 124	86.63 277	69.90 95	85.95 253	86.77 310	63.24 384	81.07 166	89.47 209	61.08 224	92.15 253	78.33 149	90.07 130	92.05 193
jason: jason.
lupinMVS		81.39 169	80.27 177	84.76 122	87.35 243	70.21 87	85.55 266	86.41 318	62.85 391	81.32 160	88.61 236	61.68 208	92.24 251	78.41 148	90.26 125	91.83 196

SDMVSNet		80.38 199	80.18 178	80.99 278	89.03 163	64.94 240	80.45 379	89.40 214	75.19 134	76.61 260	89.98 190	60.61 233	87.69 376	76.83 170	83.55 259	90.33 255

Elysia		81.53 164	80.16 179	85.62 85	85.51 302	68.25 140	88.84 133	92.19 106	71.31 230	80.50 177	89.83 194	46.89 377	94.82 110	76.85 167	89.57 138	93.80 98

StellarMVS		81.53 164	80.16 179	85.62 85	85.51 302	68.25 140	88.84 133	92.19 106	71.31 230	80.50 177	89.83 194	46.89 377	94.82 110	76.85 167	89.57 138	93.80 98

AstraMVS		80.81 180	80.14 181	82.80 228	86.05 291	63.96 264	86.46 235	85.90 328	73.71 177	80.85 172	90.56 177	54.06 294	91.57 277	79.72 131	83.97 248	92.86 155

IMVS_0403		80.80 183	80.12 182	82.87 224	87.13 257	63.59 276	85.19 273	89.33 217	70.51 255	78.49 212	89.03 221	63.26 177	93.27 195	72.56 223	85.56 223	91.74 199

PVSNet_BlendedMVS		80.60 192	80.02 183	82.36 244	88.85 165	65.40 218	86.16 249	92.00 114	69.34 287	78.11 222	86.09 312	66.02 152	94.27 133	71.52 232	82.06 281	87.39 357

EI-MVSNet		80.52 196	79.98 184	82.12 247	84.28 332	63.19 291	86.41 236	88.95 244	74.18 166	78.69 205	87.54 269	66.62 139	92.43 241	72.57 221	80.57 300	90.74 237

Fast-Effi-MVS+		80.81 180	79.92 185	83.47 192	88.85 165	64.51 252	85.53 268	89.39 215	70.79 246	78.49 212	85.06 337	67.54 128	93.58 169	67.03 283	86.58 201	92.32 178

FA-MVS(test-final)		80.96 176	79.91 186	84.10 158	88.30 193	65.01 234	84.55 294	90.01 192	73.25 194	79.61 189	87.57 266	58.35 252	94.72 117	71.29 236	86.25 208	92.56 165

IMVS_0407		80.61 190	79.90 187	82.75 235	87.13 257	63.59 276	85.33 272	89.33 217	70.51 255	77.82 228	89.03 221	61.84 204	92.91 219	72.56 223	85.56 223	91.74 199

CANet_DTU		80.61 190	79.87 188	82.83 225	85.60 300	63.17 292	87.36 199	88.65 260	76.37 99	75.88 276	88.44 242	53.51 299	93.07 212	73.30 211	89.74 136	92.25 181

viewmambaseed2359dif		80.41 197	79.84 189	82.12 247	82.95 374	62.50 304	83.39 326	88.06 271	67.11 326	80.98 167	90.31 183	66.20 148	91.01 309	74.62 196	84.90 231	92.86 155

ACMM	73.20 8	80.78 187	79.84 189	83.58 190	89.31 149	68.37 135	89.99 84	91.60 139	70.28 264	77.25 241	89.66 202	53.37 301	93.53 177	74.24 202	82.85 271	88.85 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVG-OURS-SEG-HR		80.81 180	79.76 191	83.96 179	85.60 300	68.78 119	83.54 325	90.50 173	70.66 252	76.71 256	91.66 134	60.69 229	91.26 295	76.94 166	81.58 286	91.83 196

viewdifsd2359ckpt11		80.37 201	79.73 192	82.30 245	83.70 348	62.39 305	84.20 307	86.67 312	73.22 196	80.90 169	90.62 174	63.00 186	91.56 278	76.81 171	78.44 325	92.95 152

viewmsd2359difaftdt		80.37 201	79.73 192	82.30 245	83.70 348	62.39 305	84.20 307	86.67 312	73.22 196	80.90 169	90.62 174	63.00 186	91.56 278	76.81 171	78.44 325	92.95 152

xiu_mvs_v1_base_debu		80.80 183	79.72 194	84.03 172	87.35 243	70.19 89	85.56 263	88.77 250	69.06 298	81.83 149	88.16 250	50.91 336	92.85 222	78.29 150	87.56 182	89.06 302

xiu_mvs_v1_base		80.80 183	79.72 194	84.03 172	87.35 243	70.19 89	85.56 263	88.77 250	69.06 298	81.83 149	88.16 250	50.91 336	92.85 222	78.29 150	87.56 182	89.06 302

xiu_mvs_v1_base_debi		80.80 183	79.72 194	84.03 172	87.35 243	70.19 89	85.56 263	88.77 250	69.06 298	81.83 149	88.16 250	50.91 336	92.85 222	78.29 150	87.56 182	89.06 302

LuminaMVS		80.68 188	79.62 197	83.83 182	85.07 317	68.01 149	86.99 211	88.83 247	70.36 260	81.38 159	87.99 257	50.11 347	92.51 238	79.02 138	86.89 197	90.97 227

UGNet		80.83 179	79.59 198	84.54 127	88.04 205	68.09 145	89.42 106	88.16 266	76.95 74	76.22 269	89.46 211	49.30 360	93.94 149	68.48 268	90.31 123	91.60 204
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
114514_t		80.68 188	79.51 199	84.20 155	94.09 42	67.27 178	89.64 96	91.11 155	58.75 433	74.08 320	90.72 170	58.10 253	95.04 100	69.70 255	89.42 142	90.30 257

QAPM		80.88 177	79.50 200	85.03 106	88.01 208	68.97 115	91.59 51	92.00 114	66.63 337	75.15 301	92.16 116	57.70 257	95.45 76	63.52 306	88.76 154	90.66 240

AdaColmap		80.58 195	79.42 201	84.06 167	93.09 63	68.91 116	89.36 110	88.97 243	69.27 289	75.70 279	89.69 200	57.20 265	95.77 65	63.06 315	88.41 162	87.50 355

NR-MVSNet		80.23 205	79.38 202	82.78 232	87.80 217	63.34 286	86.31 242	91.09 156	79.01 31	72.17 347	89.07 219	67.20 132	92.81 226	66.08 289	75.65 365	92.20 184

mvsmamba		80.60 192	79.38 202	84.27 151	89.74 130	67.24 180	87.47 189	86.95 305	70.02 269	75.38 289	88.93 226	51.24 333	92.56 234	75.47 190	89.22 145	93.00 149

IterMVS-LS		80.06 208	79.38 202	82.11 249	85.89 292	63.20 290	86.79 221	89.34 216	74.19 165	75.45 286	86.72 289	66.62 139	92.39 243	72.58 220	76.86 345	90.75 236
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_djsdf		80.30 204	79.32 205	83.27 201	83.98 340	65.37 221	90.50 72	90.38 177	68.55 310	76.19 270	88.70 232	56.44 272	93.46 187	78.98 141	80.14 306	90.97 227

v2v482		80.23 205	79.29 206	83.05 214	83.62 350	64.14 261	87.04 208	89.97 193	73.61 180	78.18 221	87.22 277	61.10 223	93.82 158	76.11 178	76.78 348	91.18 218

ECVR-MVS		79.61 214	79.26 207	80.67 286	90.08 117	54.69 422	87.89 178	77.44 437	74.88 145	80.27 181	92.79 101	48.96 366	92.45 240	68.55 267	92.50 84	94.86 20

XVG-OURS		80.41 197	79.23 208	83.97 178	85.64 298	69.02 113	83.03 339	90.39 176	71.09 237	77.63 234	91.49 145	54.62 289	91.35 292	75.71 184	83.47 262	91.54 207

WR-MVS		79.49 218	79.22 209	80.27 296	88.79 174	58.35 366	85.06 280	88.61 262	78.56 35	77.65 233	88.34 244	63.81 173	90.66 324	64.98 298	77.22 340	91.80 198

test1111		79.43 221	79.18 210	80.15 301	89.99 122	53.31 435	87.33 201	77.05 441	75.04 138	80.23 183	92.77 103	48.97 365	92.33 248	68.87 264	92.40 86	94.81 25

mvs_anonymous		79.42 222	79.11 211	80.34 294	84.45 331	57.97 373	82.59 341	87.62 284	67.40 325	76.17 273	88.56 239	68.47 117	89.59 342	70.65 243	86.05 212	93.47 119

v1144		80.03 209	79.03 212	83.01 216	83.78 345	64.51 252	87.11 207	90.57 172	71.96 218	78.08 224	86.20 309	61.41 215	93.94 149	74.93 194	77.23 339	90.60 243

v8		79.97 211	79.02 213	82.80 228	84.09 337	64.50 254	87.96 173	90.29 184	74.13 168	75.24 298	86.81 286	62.88 189	93.89 157	74.39 200	75.40 374	90.00 273

ab-mvs		79.51 217	78.97 214	81.14 274	88.46 186	60.91 336	83.84 314	89.24 229	70.36 260	79.03 199	88.87 229	63.23 179	90.21 331	65.12 296	82.57 276	92.28 180

icg_test_0407_2		78.92 238	78.93 215	78.90 335	87.13 257	63.59 276	76.58 428	89.33 217	70.51 255	77.82 228	89.03 221	61.84 204	81.38 434	72.56 223	85.56 223	91.74 199

Anonymous20240529		80.19 207	78.89 216	84.10 158	90.60 105	64.75 247	88.95 127	90.90 160	65.97 346	80.59 176	91.17 157	49.97 349	93.73 166	69.16 261	82.70 275	93.81 96

PCF-MVS	73.52 7	80.38 199	78.84 217	85.01 108	87.71 226	68.99 114	83.65 319	91.46 146	63.00 388	77.77 232	90.28 184	66.10 149	95.09 99	61.40 342	88.22 168	90.94 229
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v10		79.74 213	78.67 218	82.97 220	84.06 338	64.95 237	87.88 179	90.62 169	73.11 198	75.11 302	86.56 300	61.46 214	94.05 145	73.68 205	75.55 367	89.90 279

VPNet		78.69 243	78.66 219	78.76 337	88.31 192	55.72 411	84.45 298	86.63 315	76.79 79	78.26 218	90.55 178	59.30 244	89.70 341	66.63 284	77.05 342	90.88 230

BH-untuned		79.47 219	78.60 220	82.05 250	89.19 156	65.91 205	86.07 251	88.52 263	72.18 213	75.42 287	87.69 263	61.15 222	93.54 176	60.38 350	86.83 198	86.70 384

Effi-MVS+-dtu		80.03 209	78.57 221	84.42 137	85.13 315	68.74 122	88.77 136	88.10 268	74.99 139	74.97 307	83.49 375	57.27 263	93.36 191	73.53 207	80.88 294	91.18 218

WR-MVS_H		78.51 248	78.49 222	78.56 342	88.02 206	56.38 401	88.43 152	92.67 73	77.14 67	73.89 322	87.55 268	66.25 146	89.24 349	58.92 365	73.55 395	90.06 271

Vis-MVSNet (Re-imp)		78.36 251	78.45 223	78.07 354	88.64 180	51.78 447	86.70 225	79.63 419	74.14 167	75.11 302	90.83 168	61.29 219	89.75 339	58.10 375	91.60 100	92.69 161

BH-RMVSNet		79.61 214	78.44 224	83.14 208	89.38 145	65.93 204	84.95 283	87.15 300	73.56 182	78.19 220	89.79 198	56.67 270	93.36 191	59.53 358	86.74 199	90.13 263

v1192		79.59 216	78.43 225	83.07 213	83.55 352	64.52 251	86.93 215	90.58 170	70.83 245	77.78 231	85.90 313	59.15 245	93.94 149	73.96 204	77.19 341	90.76 235

v144192		79.47 219	78.37 226	82.78 232	83.35 355	63.96 264	86.96 212	90.36 180	69.99 271	77.50 235	85.67 320	60.66 231	93.77 162	74.27 201	76.58 349	90.62 241

CP-MVSNet		78.22 253	78.34 227	77.84 358	87.83 216	54.54 424	87.94 175	91.17 152	77.65 46	73.48 328	88.49 240	62.24 199	88.43 366	62.19 331	74.07 388	90.55 245

Baseline_NR-MVSNet		78.15 257	78.33 228	77.61 364	85.79 294	56.21 405	86.78 222	85.76 330	73.60 181	77.93 227	87.57 266	65.02 161	88.99 354	67.14 281	75.33 376	87.63 348

OpenMVS	72.83 10	79.77 212	78.33 228	84.09 162	85.17 311	69.91 94	90.57 69	90.97 158	66.70 331	72.17 347	91.91 122	54.70 287	93.96 146	61.81 338	90.95 114	88.41 331

UniMVSNet_ETH3D		79.10 232	78.24 230	81.70 257	86.85 268	60.24 350	87.28 203	88.79 249	74.25 164	76.84 251	90.53 179	49.48 356	91.56 278	67.98 271	82.15 279	93.29 126

V42		79.38 225	78.24 230	82.83 225	81.10 406	65.50 217	85.55 266	89.82 197	71.57 226	78.21 219	86.12 311	60.66 231	93.18 206	75.64 185	75.46 371	89.81 284

PS-CasMVS		78.01 262	78.09 232	77.77 360	87.71 226	54.39 426	88.02 171	91.22 149	77.50 54	73.26 330	88.64 235	60.73 227	88.41 367	61.88 336	73.88 392	90.53 246

v1921920		79.22 228	78.03 233	82.80 228	83.30 357	63.94 266	86.80 220	90.33 181	69.91 274	77.48 236	85.53 324	58.44 251	93.75 164	73.60 206	76.85 346	90.71 239

jajsoiax		79.29 227	77.96 234	83.27 201	84.68 325	66.57 192	89.25 113	90.16 188	69.20 294	75.46 285	89.49 208	45.75 394	93.13 209	76.84 169	80.80 296	90.11 265

TAPA-MVS	73.13 9	79.15 230	77.94 235	82.79 231	89.59 132	62.99 297	88.16 167	91.51 142	65.77 347	77.14 249	91.09 159	60.91 226	93.21 200	50.26 427	87.05 193	92.17 189
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tttt0517		79.40 223	77.91 236	83.90 181	88.10 202	63.84 268	88.37 158	84.05 352	71.45 228	76.78 254	89.12 218	49.93 352	94.89 107	70.18 249	83.18 268	92.96 151

c3_l		78.75 240	77.91 236	81.26 270	82.89 375	61.56 322	84.09 311	89.13 235	69.97 272	75.56 281	84.29 352	66.36 144	92.09 255	73.47 209	75.48 369	90.12 264

VortexMVS		78.57 247	77.89 238	80.59 287	85.89 292	62.76 300	85.61 261	89.62 207	72.06 216	74.99 306	85.38 328	55.94 276	90.77 322	74.99 193	76.58 349	88.23 335

MVSTER		79.01 234	77.88 239	82.38 243	83.07 365	64.80 246	84.08 312	88.95 244	69.01 301	78.69 205	87.17 280	54.70 287	92.43 241	74.69 195	80.57 300	89.89 280

tt0805		78.73 241	77.83 240	81.43 263	85.17 311	60.30 349	89.41 107	90.90 160	71.21 234	77.17 248	88.73 231	46.38 383	93.21 200	72.57 221	78.96 320	90.79 233

X-MVStestdata		80.37 201	77.83 240	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 26	83.67 114	12.47 498	67.45 129	96.60 38	83.06 87	94.50 56	94.07 80

v148		78.72 242	77.80 242	81.47 262	82.73 378	61.96 316	86.30 243	88.08 269	73.26 193	76.18 271	85.47 326	62.46 194	92.36 245	71.92 231	73.82 393	90.09 267

v1240		78.99 235	77.78 243	82.64 237	83.21 360	63.54 280	86.62 229	90.30 183	69.74 281	77.33 239	85.68 319	57.04 266	93.76 163	73.13 214	76.92 343	90.62 241

mvs_tets		79.13 231	77.77 244	83.22 205	84.70 324	66.37 194	89.17 116	90.19 187	69.38 286	75.40 288	89.46 211	44.17 406	93.15 207	76.78 173	80.70 298	90.14 262

miper_ehance_all_eth		78.59 246	77.76 245	81.08 276	82.66 380	61.56 322	83.65 319	89.15 233	68.87 305	75.55 282	83.79 366	66.49 142	92.03 256	73.25 212	76.39 354	89.64 288

thisisatest0530		79.40 223	77.76 245	84.31 145	87.69 230	65.10 233	87.36 199	84.26 350	70.04 268	77.42 237	88.26 248	49.94 350	94.79 114	70.20 248	84.70 235	93.03 146

CDS-MVSNet		79.07 233	77.70 247	83.17 207	87.60 233	68.23 142	84.40 303	86.20 323	67.49 323	76.36 266	86.54 301	61.54 211	90.79 319	61.86 337	87.33 187	90.49 248
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Anonymous20231211		78.97 236	77.69 248	82.81 227	90.54 107	64.29 259	90.11 83	91.51 142	65.01 363	76.16 274	88.13 255	50.56 341	93.03 217	69.68 256	77.56 338	91.11 220

PEN-MVS		77.73 268	77.69 248	77.84 358	87.07 265	53.91 429	87.91 177	91.18 151	77.56 51	73.14 332	88.82 230	61.23 220	89.17 351	59.95 353	72.37 403	90.43 250

AUN-MVS		79.21 229	77.60 250	84.05 170	88.71 178	67.61 163	85.84 258	87.26 297	69.08 297	77.23 243	88.14 254	53.20 303	93.47 186	75.50 189	73.45 396	91.06 222

v7n		78.97 236	77.58 251	83.14 208	83.45 354	65.51 216	88.32 160	91.21 150	73.69 178	72.41 343	86.32 307	57.93 254	93.81 159	69.18 260	75.65 365	90.11 265

mamba_0408		79.37 226	77.52 252	84.93 113	88.81 169	67.96 150	65.03 481	88.66 258	70.96 243	79.48 192	89.80 196	58.69 247	94.65 121	70.35 246	85.93 216	92.18 186

SSM_04072		77.67 273	77.52 252	78.12 352	88.81 169	67.96 150	65.03 481	88.66 258	70.96 243	79.48 192	89.80 196	58.69 247	74.23 475	70.35 246	85.93 216	92.18 186

TAMVS		78.89 239	77.51 254	83.03 215	87.80 217	67.79 158	84.72 287	85.05 339	67.63 320	76.75 255	87.70 262	62.25 198	90.82 318	58.53 370	87.13 192	90.49 248

sd_testset		77.70 271	77.40 255	78.60 340	89.03 163	60.02 352	79.00 400	85.83 329	75.19 134	76.61 260	89.98 190	54.81 282	85.46 401	62.63 324	83.55 259	90.33 255

GBi-Net		78.40 249	77.40 255	81.40 265	87.60 233	63.01 293	88.39 155	89.28 223	71.63 222	75.34 291	87.28 273	54.80 283	91.11 301	62.72 320	79.57 310	90.09 267

test1		78.40 249	77.40 255	81.40 265	87.60 233	63.01 293	88.39 155	89.28 223	71.63 222	75.34 291	87.28 273	54.80 283	91.11 301	62.72 320	79.57 310	90.09 267

BH-w/o		78.21 254	77.33 258	80.84 282	88.81 169	65.13 230	84.87 284	87.85 279	69.75 279	74.52 315	84.74 344	61.34 217	93.11 210	58.24 374	85.84 219	84.27 424

FMVSNet2		78.20 255	77.21 259	81.20 272	87.60 233	62.89 299	87.47 189	89.02 239	71.63 222	75.29 297	87.28 273	54.80 283	91.10 304	62.38 328	79.38 316	89.61 289

anonymousdsp		78.60 245	77.15 260	82.98 219	80.51 412	67.08 183	87.24 204	89.53 210	65.66 349	75.16 300	87.19 279	52.52 305	92.25 250	77.17 163	79.34 317	89.61 289

HY-MVS	69.67 12	77.95 263	77.15 260	80.36 293	87.57 242	60.21 351	83.37 328	87.78 281	66.11 341	75.37 290	87.06 284	63.27 176	90.48 326	61.38 343	82.43 277	90.40 252

cl22		78.07 259	77.01 262	81.23 271	82.37 387	61.83 318	83.55 323	87.98 273	68.96 304	75.06 304	83.87 362	61.40 216	91.88 265	73.53 207	76.39 354	89.98 276

Anonymous202405211		78.25 252	77.01 262	81.99 252	91.03 95	60.67 342	84.77 286	83.90 354	70.65 253	80.00 185	91.20 155	41.08 427	91.43 290	65.21 295	85.26 228	93.85 92

MVS		78.19 256	76.99 264	81.78 255	85.66 297	66.99 184	84.66 289	90.47 174	55.08 455	72.02 349	85.27 330	63.83 172	94.11 143	66.10 288	89.80 135	84.24 425

LCM-MVSNet-Re		77.05 283	76.94 265	77.36 368	87.20 254	51.60 448	80.06 385	80.46 407	75.20 133	67.69 400	86.72 289	62.48 193	88.98 355	63.44 308	89.25 143	91.51 208

miper_enhance_ethall		77.87 266	76.86 266	80.92 281	81.65 394	61.38 326	82.68 340	88.98 241	65.52 351	75.47 283	82.30 395	65.76 156	92.00 259	72.95 216	76.39 354	89.39 295

FMVSNet3		77.88 265	76.85 267	80.97 280	86.84 269	62.36 307	86.52 233	88.77 250	71.13 235	75.34 291	86.66 295	54.07 293	91.10 304	62.72 320	79.57 310	89.45 293

DTE-MVSNet		76.99 284	76.80 268	77.54 367	86.24 284	53.06 439	87.52 187	90.66 168	77.08 71	72.50 341	88.67 234	60.48 235	89.52 343	57.33 382	70.74 415	90.05 272

CNLPA		78.08 258	76.79 269	81.97 253	90.40 110	71.07 72	87.59 186	84.55 344	66.03 344	72.38 344	89.64 203	57.56 259	86.04 393	59.61 357	83.35 264	88.79 318

cl____		77.72 269	76.76 270	80.58 288	82.49 384	60.48 346	83.09 335	87.87 277	69.22 292	74.38 318	85.22 333	62.10 201	91.53 283	71.09 237	75.41 373	89.73 287

DIV-MVS_self_test		77.72 269	76.76 270	80.58 288	82.48 385	60.48 346	83.09 335	87.86 278	69.22 292	74.38 318	85.24 331	62.10 201	91.53 283	71.09 237	75.40 374	89.74 286

baseline1		76.98 285	76.75 272	77.66 362	88.13 200	55.66 412	85.12 277	81.89 387	73.04 200	76.79 253	88.90 227	62.43 195	87.78 375	63.30 310	71.18 413	89.55 291

eth_miper_zixun_eth		77.92 264	76.69 273	81.61 260	83.00 368	61.98 315	83.15 332	89.20 231	69.52 284	74.86 309	84.35 351	61.76 207	92.56 234	71.50 234	72.89 401	90.28 258

pm-mvs1		77.25 281	76.68 274	78.93 334	84.22 334	58.62 364	86.41 236	88.36 265	71.37 229	73.31 329	88.01 256	61.22 221	89.15 352	64.24 304	73.01 400	89.03 306

ET-MVSNet_ETH3D		78.63 244	76.63 275	84.64 125	86.73 273	69.47 103	85.01 281	84.61 343	69.54 283	66.51 421	86.59 297	50.16 346	91.75 269	76.26 176	84.24 245	92.69 161

test2506		77.30 280	76.49 276	79.74 316	90.08 117	52.02 441	87.86 180	63.10 484	74.88 145	80.16 184	92.79 101	38.29 445	92.35 246	68.74 266	92.50 84	94.86 20

Fast-Effi-MVS+-dtu		78.02 261	76.49 276	82.62 238	83.16 364	66.96 187	86.94 214	87.45 289	72.45 207	71.49 355	84.17 359	54.79 286	91.58 275	67.61 274	80.31 303	89.30 298

1112_ss		77.40 278	76.43 278	80.32 295	89.11 162	60.41 348	83.65 319	87.72 283	62.13 403	73.05 333	86.72 289	62.58 192	89.97 335	62.11 334	80.80 296	90.59 244

IMVS_0404		77.16 282	76.42 279	79.37 326	87.13 257	63.59 276	77.12 425	89.33 217	70.51 255	66.22 424	89.03 221	50.36 344	82.78 424	72.56 223	85.56 223	91.74 199

PAPM		77.68 272	76.40 280	81.51 261	87.29 253	61.85 317	83.78 315	89.59 208	64.74 365	71.23 357	88.70 232	62.59 191	93.66 168	52.66 411	87.03 194	89.01 307

PLC	70.83 11	78.05 260	76.37 281	83.08 212	91.88 84	67.80 157	88.19 165	89.46 212	64.33 372	69.87 374	88.38 243	53.66 297	93.58 169	58.86 366	82.73 273	87.86 344
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 276	76.18 282	81.20 272	88.24 194	63.24 288	84.61 292	86.40 319	67.55 322	77.81 230	86.48 303	54.10 292	93.15 207	57.75 378	82.72 274	87.20 367

FMVSNet1		77.44 276	76.12 283	81.40 265	86.81 270	63.01 293	88.39 155	89.28 223	70.49 259	74.39 317	87.28 273	49.06 364	91.11 301	60.91 346	78.52 323	90.09 267

MonoMVSNet		76.49 295	75.80 284	78.58 341	81.55 397	58.45 365	86.36 241	86.22 322	74.87 147	74.73 311	83.73 368	51.79 325	88.73 360	70.78 239	72.15 406	88.55 328

test_vis1_n_1920		75.52 311	75.78 285	74.75 398	79.84 420	57.44 385	83.26 330	85.52 332	62.83 392	79.34 197	86.17 310	45.10 399	79.71 441	78.75 143	81.21 290	87.10 375

CHOSEN 1792x2688		77.63 274	75.69 286	83.44 194	89.98 123	68.58 130	78.70 405	87.50 287	56.38 449	75.80 278	86.84 285	58.67 249	91.40 291	61.58 341	85.75 221	90.34 254

FE-MVS		77.78 267	75.68 287	84.08 163	88.09 203	66.00 202	83.13 333	87.79 280	68.42 314	78.01 225	85.23 332	45.50 397	95.12 93	59.11 363	85.83 220	91.11 220

WTY-MVS		75.65 309	75.68 287	75.57 384	86.40 282	56.82 392	77.92 418	82.40 380	65.10 360	76.18 271	87.72 261	63.13 184	80.90 437	60.31 351	81.96 282	89.00 309

testing91		76.54 290	75.66 289	79.18 331	88.43 188	55.89 408	81.08 366	83.00 372	73.76 176	75.34 291	84.29 352	46.20 388	90.07 333	64.33 302	84.50 237	91.58 206

XXY-MVS		75.41 314	75.56 290	74.96 393	83.59 351	57.82 377	80.59 376	83.87 355	66.54 338	74.93 308	88.31 245	63.24 178	80.09 440	62.16 332	76.85 346	86.97 377

thres100view900		76.50 292	75.55 291	79.33 327	89.52 135	56.99 390	85.83 259	83.23 365	73.94 171	76.32 267	87.12 281	51.89 322	91.95 261	48.33 437	83.75 253	89.07 300

thres600view7		76.50 292	75.44 292	79.68 319	89.40 143	57.16 387	85.53 268	83.23 365	73.79 175	76.26 268	87.09 282	51.89 322	91.89 264	48.05 442	83.72 256	90.00 273

Test_1112_low_res		76.40 299	75.44 292	79.27 328	89.28 151	58.09 369	81.69 356	87.07 303	59.53 424	72.48 342	86.67 294	61.30 218	89.33 346	60.81 348	80.15 305	90.41 251

HyFIR lowres test		77.53 275	75.40 294	83.94 180	89.59 132	66.62 190	80.36 380	88.64 261	56.29 450	76.45 263	85.17 334	57.64 258	93.28 193	61.34 344	83.10 269	91.91 195

thisisatest0515		77.33 279	75.38 295	83.18 206	85.27 310	63.80 269	82.11 349	83.27 364	65.06 361	75.91 275	83.84 364	49.54 355	94.27 133	67.24 279	86.19 209	91.48 211

tfpn200view9		76.42 298	75.37 296	79.55 324	89.13 158	57.65 381	85.17 274	83.60 357	73.41 188	76.45 263	86.39 305	52.12 312	91.95 261	48.33 437	83.75 253	89.07 300

thres400		76.50 292	75.37 296	79.86 309	89.13 158	57.65 381	85.17 274	83.60 357	73.41 188	76.45 263	86.39 305	52.12 312	91.95 261	48.33 437	83.75 253	90.00 273

usedtu_dtu_shiyan1		76.43 296	75.32 298	79.76 314	83.00 368	60.72 339	81.74 353	88.76 254	68.99 302	72.98 334	84.19 357	56.41 273	90.27 327	62.39 326	79.40 314	88.31 332

FE-MVSNET3		76.43 296	75.32 298	79.76 314	83.00 368	60.72 339	81.74 353	88.76 254	68.99 302	72.98 334	84.19 357	56.41 273	90.27 327	62.39 326	79.40 314	88.31 332

1314		76.53 291	75.30 300	80.21 299	83.93 341	62.32 309	84.66 289	88.81 248	60.23 417	70.16 368	84.07 361	55.30 280	90.73 323	67.37 277	83.21 267	87.59 351

testing3-2		75.12 319	75.19 301	74.91 394	90.40 110	45.09 477	80.29 382	78.42 429	78.37 40	76.54 262	87.75 260	44.36 404	87.28 381	57.04 385	83.49 261	92.37 175

GA-MVS		76.87 287	75.17 302	81.97 253	82.75 377	62.58 301	81.44 361	86.35 321	72.16 215	74.74 310	82.89 386	46.20 388	92.02 258	68.85 265	81.09 291	91.30 216

testing99		76.09 304	75.12 303	79.00 332	88.16 197	55.50 414	80.79 370	81.40 394	73.30 192	75.17 299	84.27 355	44.48 403	90.02 334	64.28 303	84.22 246	91.48 211

EPNet_dtu		75.46 312	74.86 304	77.23 371	82.57 382	54.60 423	86.89 216	83.09 369	71.64 221	66.25 423	85.86 315	55.99 275	88.04 371	54.92 399	86.55 202	89.05 305
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		76.95 286	74.82 305	83.37 198	90.45 108	67.36 174	89.15 120	86.94 306	61.87 406	69.52 377	90.61 176	51.71 326	94.53 124	46.38 449	86.71 200	88.21 337

SD_0403		74.65 322	74.77 306	74.29 402	86.20 286	47.42 466	83.71 317	85.12 336	69.30 288	68.50 391	87.95 258	59.40 243	86.05 392	49.38 431	83.35 264	89.40 294

cascas		76.72 289	74.64 307	82.99 217	85.78 295	65.88 206	82.33 345	89.21 230	60.85 412	72.74 337	81.02 407	47.28 373	93.75 164	67.48 276	85.02 229	89.34 297

DP-MVS		76.78 288	74.57 308	83.42 195	93.29 52	69.46 105	88.55 150	83.70 356	63.98 378	70.20 365	88.89 228	54.01 295	94.80 113	46.66 446	81.88 284	86.01 397

TransMVSNet (Re)		75.39 316	74.56 309	77.86 357	85.50 304	57.10 389	86.78 222	86.09 326	72.17 214	71.53 354	87.34 272	63.01 185	89.31 347	56.84 388	61.83 457	87.17 369

LTVRE_ROB	69.57 13	76.25 301	74.54 310	81.41 264	88.60 181	64.38 258	79.24 395	89.12 236	70.76 248	69.79 376	87.86 259	49.09 363	93.20 203	56.21 394	80.16 304	86.65 386
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
thres200		75.55 310	74.47 311	78.82 336	87.78 220	57.85 376	83.07 337	83.51 360	72.44 209	75.84 277	84.42 347	52.08 315	91.75 269	47.41 444	83.64 258	86.86 379

MVP-Stereo		76.12 302	74.46 312	81.13 275	85.37 307	69.79 96	84.42 302	87.95 275	65.03 362	67.46 404	85.33 329	53.28 302	91.73 271	58.01 376	83.27 266	81.85 451
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
reproduce_monomvs		75.40 315	74.38 313	78.46 347	83.92 342	57.80 378	83.78 315	86.94 306	73.47 186	72.25 346	84.47 346	38.74 441	89.27 348	75.32 191	70.53 416	88.31 332

F-COLMAP		76.38 300	74.33 314	82.50 241	89.28 151	66.95 188	88.41 154	89.03 238	64.05 376	66.83 413	88.61 236	46.78 379	92.89 220	57.48 379	78.55 322	87.67 347

XVG-ACMP-BASELINE		76.11 303	74.27 315	81.62 258	83.20 361	64.67 248	83.60 322	89.75 202	69.75 279	71.85 350	87.09 282	32.78 461	92.11 254	69.99 252	80.43 302	88.09 339

testing11		75.14 318	74.01 316	78.53 344	88.16 197	56.38 401	80.74 373	80.42 409	70.67 249	72.69 340	83.72 369	43.61 410	89.86 336	62.29 330	83.76 252	89.36 296

ACMH+	68.96 14	76.01 305	74.01 316	82.03 251	88.60 181	65.31 226	88.86 130	87.55 285	70.25 266	67.75 399	87.47 271	41.27 425	93.19 205	58.37 372	75.94 362	87.60 349

ACMH	67.68 16	75.89 306	73.93 318	81.77 256	88.71 178	66.61 191	88.62 146	89.01 240	69.81 275	66.78 414	86.70 293	41.95 422	91.51 285	55.64 395	78.14 331	87.17 369
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		75.24 317	73.90 319	79.27 328	82.65 381	58.27 368	80.80 369	82.73 378	61.57 407	75.33 295	83.13 381	55.52 278	91.07 307	64.98 298	78.34 330	88.45 329

IterMVS-SCA-FT		75.43 313	73.87 320	80.11 302	82.69 379	64.85 245	81.57 358	83.47 361	69.16 295	70.49 362	84.15 360	51.95 318	88.15 369	69.23 259	72.14 407	87.34 362

baseline2		75.70 308	73.83 321	81.30 268	83.26 358	61.79 319	82.57 342	80.65 402	66.81 328	66.88 412	83.42 376	57.86 256	92.19 252	63.47 307	79.57 310	89.91 278

test_cas_vis1_n_1920		73.76 333	73.74 322	73.81 409	75.90 453	59.77 354	80.51 377	82.40 380	58.30 435	81.62 157	85.69 318	44.35 405	76.41 459	76.29 175	78.61 321	85.23 411

sss		73.60 335	73.64 323	73.51 411	82.80 376	55.01 420	76.12 430	81.69 390	62.47 398	74.68 312	85.85 316	57.32 262	78.11 448	60.86 347	80.93 292	87.39 357

myMVS_eth3d28		73.62 334	73.53 324	73.90 408	88.20 195	47.41 467	78.06 415	79.37 421	74.29 163	73.98 321	84.29 352	44.67 400	83.54 418	51.47 417	87.39 186	90.74 237

SSC-MVS3.2		73.35 343	73.39 325	73.23 412	85.30 309	49.01 462	74.58 445	81.57 391	75.21 132	73.68 325	85.58 323	52.53 304	82.05 429	54.33 403	77.69 336	88.63 325

pmmvs6		74.69 321	73.39 325	78.61 339	81.38 401	57.48 384	86.64 228	87.95 275	64.99 364	70.18 366	86.61 296	50.43 343	89.52 343	62.12 333	70.18 418	88.83 316

IB-MVS	68.01 15	75.85 307	73.36 327	83.31 199	84.76 323	66.03 199	83.38 327	85.06 338	70.21 267	69.40 378	81.05 406	45.76 393	94.66 120	65.10 297	75.49 368	89.25 299
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
D2MVS		74.82 320	73.21 328	79.64 321	79.81 421	62.56 303	80.34 381	87.35 291	64.37 371	68.86 384	82.66 390	46.37 384	90.10 332	67.91 272	81.24 289	86.25 390

tfpnnormal		74.39 323	73.16 329	78.08 353	86.10 290	58.05 370	84.65 291	87.53 286	70.32 263	71.22 358	85.63 321	54.97 281	89.86 336	43.03 461	75.02 381	86.32 389

miper_lstm_enhance		74.11 328	73.11 330	77.13 372	80.11 416	59.62 356	72.23 452	86.92 308	66.76 330	70.40 363	82.92 385	56.93 267	82.92 423	69.06 262	72.63 402	88.87 314

mmtdpeth		74.16 327	73.01 331	77.60 366	83.72 347	61.13 328	85.10 278	85.10 337	72.06 216	77.21 247	80.33 416	43.84 408	85.75 395	77.14 164	52.61 476	85.91 400

IterMVS		74.29 324	72.94 332	78.35 348	81.53 398	63.49 282	81.58 357	82.49 379	68.06 318	69.99 371	83.69 370	51.66 327	85.54 399	65.85 291	71.64 410	86.01 397
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WBMVS		73.43 337	72.81 333	75.28 390	87.91 211	50.99 454	78.59 408	81.31 396	65.51 353	74.47 316	84.83 341	46.39 382	86.68 385	58.41 371	77.86 332	88.17 338

MS-PatchMatch		73.83 332	72.67 334	77.30 370	83.87 343	66.02 200	81.82 351	84.66 342	61.37 410	68.61 387	82.82 388	47.29 372	88.21 368	59.27 360	84.32 244	77.68 466

testing222		74.04 329	72.66 335	78.19 350	87.89 212	55.36 415	81.06 367	79.20 424	71.30 232	74.65 313	83.57 374	39.11 440	88.67 362	51.43 419	85.75 221	90.53 246

CVMVSNet		72.99 351	72.58 336	74.25 403	84.28 332	50.85 455	86.41 236	83.45 362	44.56 475	73.23 331	87.54 269	49.38 358	85.70 396	65.90 290	78.44 325	86.19 392

test-LLR		72.94 352	72.43 337	74.48 399	81.35 402	58.04 371	78.38 409	77.46 435	66.66 332	69.95 372	79.00 431	48.06 369	79.24 442	66.13 286	84.83 232	86.15 393

OurMVSNet-221017-0		74.26 325	72.42 338	79.80 311	83.76 346	59.59 357	85.92 255	86.64 314	66.39 339	66.96 411	87.58 265	39.46 436	91.60 274	65.76 292	69.27 421	88.22 336

SCA		74.22 326	72.33 339	79.91 307	84.05 339	62.17 311	79.96 388	79.29 423	66.30 340	72.38 344	80.13 419	51.95 318	88.60 363	59.25 361	77.67 337	88.96 311

UBG		73.08 349	72.27 340	75.51 386	88.02 206	51.29 452	78.35 412	77.38 438	65.52 351	73.87 323	82.36 393	45.55 395	86.48 388	55.02 398	84.39 243	88.75 320

tpmrst		72.39 357	72.13 341	73.18 416	80.54 411	49.91 459	79.91 389	79.08 425	63.11 386	71.69 352	79.95 421	55.32 279	82.77 425	65.66 293	73.89 391	86.87 378

pmmvs4		74.03 331	71.91 342	80.39 291	81.96 390	68.32 136	81.45 360	82.14 385	59.32 425	69.87 374	85.13 335	52.40 308	88.13 370	60.21 352	74.74 384	84.73 421

EG-PatchMatch MVS		74.04 329	71.82 343	80.71 285	84.92 319	67.42 170	85.86 257	88.08 269	66.04 343	64.22 439	83.85 363	35.10 457	92.56 234	57.44 380	80.83 295	82.16 449

tpm		72.37 359	71.71 344	74.35 401	82.19 388	52.00 442	79.22 396	77.29 439	64.56 367	72.95 336	83.68 371	51.35 328	83.26 422	58.33 373	75.80 363	87.81 345

WB-MVSnew		71.96 366	71.65 345	72.89 418	84.67 328	51.88 445	82.29 346	77.57 434	62.31 400	73.67 326	83.00 383	53.49 300	81.10 436	45.75 453	82.13 280	85.70 403

UWE-MVS		72.13 364	71.49 346	74.03 406	86.66 276	47.70 464	81.40 362	76.89 443	63.60 382	75.59 280	84.22 356	39.94 433	85.62 398	48.98 434	86.13 211	88.77 319

CL-MVSNet_self_test		72.37 359	71.46 347	75.09 392	79.49 427	53.53 431	80.76 372	85.01 340	69.12 296	70.51 361	82.05 399	57.92 255	84.13 412	52.27 413	66.00 438	87.60 349

tpm2		73.26 345	71.46 347	78.63 338	83.34 356	56.71 395	80.65 375	80.40 410	56.63 448	73.55 327	82.02 400	51.80 324	91.24 296	56.35 393	78.42 328	87.95 341

RPSCF		73.23 347	71.46 347	78.54 343	82.50 383	59.85 353	82.18 348	82.84 377	58.96 429	71.15 359	89.41 215	45.48 398	84.77 408	58.82 367	71.83 409	91.02 226

PatchmatchNet		73.12 348	71.33 350	78.49 346	83.18 362	60.85 337	79.63 390	78.57 428	64.13 373	71.73 351	79.81 424	51.20 334	85.97 394	57.40 381	76.36 359	88.66 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET2		72.88 355	71.28 351	77.67 361	78.30 438	57.78 379	84.43 300	88.92 246	69.56 282	64.61 436	81.67 402	46.73 381	88.54 365	59.33 359	67.99 430	86.69 385

CR-MVSNet		73.37 340	71.27 352	79.67 320	81.32 404	65.19 228	75.92 432	80.30 411	59.92 420	72.73 338	81.19 404	52.50 306	86.69 384	59.84 354	77.71 334	87.11 373

SixPastTwentyTwo		73.37 340	71.26 353	79.70 318	85.08 316	57.89 375	85.57 262	83.56 359	71.03 241	65.66 427	85.88 314	42.10 420	92.57 233	59.11 363	63.34 451	88.65 324

blended_shiyan8		73.38 338	71.17 354	80.02 304	78.36 436	61.51 324	82.43 343	87.28 292	65.40 355	68.61 387	77.53 444	51.91 321	91.00 312	63.28 311	65.76 440	87.53 353

blended_shiyan6		73.38 338	71.17 354	80.01 305	78.36 436	61.48 325	82.43 343	87.27 295	65.40 355	68.56 389	77.55 443	51.94 320	91.01 309	63.27 312	65.76 440	87.55 352

ETVMVS		72.25 362	71.05 356	75.84 380	87.77 222	51.91 444	79.39 393	74.98 450	69.26 290	73.71 324	82.95 384	40.82 429	86.14 391	46.17 450	84.43 242	89.47 292

MSDG		73.36 342	70.99 357	80.49 290	84.51 330	65.80 210	80.71 374	86.13 325	65.70 348	65.46 429	83.74 367	44.60 401	90.91 315	51.13 420	76.89 344	84.74 420

usedtu_blend_shiyan5		73.29 344	70.96 358	80.25 297	77.80 443	62.16 312	84.44 299	87.38 290	64.41 369	68.09 394	76.28 452	51.32 329	91.23 297	63.21 313	65.76 440	87.35 359

PatchMatch-RL		72.38 358	70.90 359	76.80 375	88.60 181	67.38 173	79.53 391	76.17 447	62.75 394	69.36 379	82.00 401	45.51 396	84.89 407	53.62 406	80.58 299	78.12 465

PVSNet	64.34 18	72.08 365	70.87 360	75.69 382	86.21 285	56.44 399	74.37 446	80.73 401	62.06 404	70.17 367	82.23 397	42.86 414	83.31 421	54.77 400	84.45 241	87.32 363

gbinet_0.2-2-1-0.02		73.24 346	70.86 361	80.39 291	78.03 441	61.62 321	83.10 334	86.69 311	65.98 345	69.29 381	76.15 455	49.77 353	91.51 285	62.75 319	66.00 438	88.03 340

wanda-best-256-512		72.94 352	70.66 362	79.79 312	77.80 443	61.03 333	81.31 363	87.15 300	65.18 358	68.09 394	76.28 452	51.32 329	90.97 313	63.06 315	65.76 440	87.35 359

FE-blended-shiyan7		72.94 352	70.66 362	79.79 312	77.80 443	61.03 333	81.31 363	87.15 300	65.18 358	68.09 394	76.28 452	51.32 329	90.97 313	63.06 315	65.76 440	87.35 359

dmvs_re		71.14 370	70.58 364	72.80 419	81.96 390	59.68 355	75.60 436	79.34 422	68.55 310	69.27 382	80.72 412	49.42 357	76.54 456	52.56 412	77.79 333	82.19 448

test_fmvs1		70.93 373	70.52 365	72.16 423	73.71 465	55.05 419	80.82 368	78.77 427	51.21 467	78.58 209	84.41 348	31.20 466	76.94 454	75.88 183	80.12 307	84.47 423

RPMNet		73.51 336	70.49 366	82.58 240	81.32 404	65.19 228	75.92 432	92.27 94	57.60 442	72.73 338	76.45 449	52.30 309	95.43 78	48.14 441	77.71 334	87.11 373

test_0402		72.79 356	70.44 367	79.84 310	88.13 200	65.99 203	85.93 254	84.29 348	65.57 350	67.40 407	85.49 325	46.92 376	92.61 230	35.88 476	74.38 387	80.94 456

COLMAP_ROB	66.92 17	73.01 350	70.41 368	80.81 283	87.13 257	65.63 213	88.30 162	84.19 351	62.96 389	63.80 444	87.69 263	38.04 446	92.56 234	46.66 446	74.91 382	84.24 425
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test-mter		71.41 368	70.39 369	74.48 399	81.35 402	58.04 371	78.38 409	77.46 435	60.32 416	69.95 372	79.00 431	36.08 455	79.24 442	66.13 286	84.83 232	86.15 393

test_fmvs1_n		70.86 375	70.24 370	72.73 420	72.51 476	55.28 417	81.27 365	79.71 418	51.49 466	78.73 204	84.87 340	27.54 472	77.02 453	76.06 179	79.97 308	85.88 401

pmmvs5		71.55 367	70.20 371	75.61 383	77.83 442	56.39 400	81.74 353	80.89 398	57.76 440	67.46 404	84.49 345	49.26 361	85.32 403	57.08 384	75.29 377	85.11 415

MDTV_nov1_ep13			69.97 372		83.18 362	53.48 432	77.10 426	80.18 415	60.45 414	69.33 380	80.44 413	48.89 367	86.90 383	51.60 416	78.51 324

blend_shiyan4		72.29 361	69.65 373	80.21 299	78.24 439	62.16 312	82.29 346	87.27 295	65.41 354	68.43 393	76.42 451	39.91 434	91.23 297	63.21 313	65.66 445	87.22 366

sc_t1		72.19 363	69.51 374	80.23 298	84.81 321	61.09 330	84.68 288	80.22 413	60.70 413	71.27 356	83.58 373	36.59 452	89.24 349	60.41 349	63.31 452	90.37 253

MIMVSNet		70.69 377	69.30 375	74.88 395	84.52 329	56.35 403	75.87 434	79.42 420	64.59 366	67.76 398	82.41 392	41.10 426	81.54 432	46.64 448	81.34 287	86.75 383

tpmvs		71.09 371	69.29 376	76.49 376	82.04 389	56.04 406	78.92 403	81.37 395	64.05 376	67.18 409	78.28 437	49.74 354	89.77 338	49.67 430	72.37 403	83.67 432

test_vis1_n		69.85 391	69.21 377	71.77 425	72.66 475	55.27 418	81.48 359	76.21 446	52.03 463	75.30 296	83.20 380	28.97 469	76.22 461	74.60 197	78.41 329	83.81 431

Patchmtry		70.74 376	69.16 378	75.49 387	80.72 408	54.07 428	74.94 443	80.30 411	58.34 434	70.01 369	81.19 404	52.50 306	86.54 386	53.37 408	71.09 414	85.87 402

TESTMET0.1,1		69.89 390	69.00 379	72.55 421	79.27 431	56.85 391	78.38 409	74.71 454	57.64 441	68.09 394	77.19 446	37.75 447	76.70 455	63.92 305	84.09 247	84.10 428

PMMVS		69.34 394	68.67 380	71.35 430	75.67 456	62.03 314	75.17 438	73.46 457	50.00 468	68.68 385	79.05 429	52.07 316	78.13 447	61.16 345	82.77 272	73.90 472

K. test v3		71.19 369	68.51 381	79.21 330	83.04 367	57.78 379	84.35 304	76.91 442	72.90 203	62.99 447	82.86 387	39.27 437	91.09 306	61.65 340	52.66 475	88.75 320

USDC		70.33 382	68.37 382	76.21 378	80.60 410	56.23 404	79.19 397	86.49 317	60.89 411	61.29 452	85.47 326	31.78 464	89.47 345	53.37 408	76.21 360	82.94 442

tpm cat1		70.57 378	68.31 383	77.35 369	82.41 386	57.95 374	78.08 414	80.22 413	52.04 462	68.54 390	77.66 442	52.00 317	87.84 374	51.77 414	72.07 408	86.25 390

OpenMVS_ROB	64.09 19	70.56 379	68.19 384	77.65 363	80.26 413	59.41 360	85.01 281	82.96 374	58.76 432	65.43 430	82.33 394	37.63 448	91.23 297	45.34 456	76.03 361	82.32 446

EPMVS		69.02 396	68.16 385	71.59 426	79.61 425	49.80 461	77.40 422	66.93 475	62.82 393	70.01 369	79.05 429	45.79 392	77.86 450	56.58 391	75.26 378	87.13 372

CMPMVS	51.72 21	70.19 384	68.16 385	76.28 377	73.15 472	57.55 383	79.47 392	83.92 353	48.02 471	56.48 470	84.81 342	43.13 412	86.42 389	62.67 323	81.81 285	84.89 418
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
AllTest		70.96 372	68.09 387	79.58 322	85.15 313	63.62 272	84.58 293	79.83 416	62.31 400	60.32 457	86.73 287	32.02 462	88.96 357	50.28 425	71.57 411	86.15 393

tt0320		70.49 381	68.03 388	77.89 356	84.78 322	59.12 361	83.55 323	80.44 408	58.13 437	67.43 406	80.41 415	39.26 438	87.54 378	55.12 397	63.18 453	86.99 376

gg-mvs-nofinetune		69.95 389	67.96 389	75.94 379	83.07 365	54.51 425	77.23 424	70.29 465	63.11 386	70.32 364	62.33 479	43.62 409	88.69 361	53.88 405	87.76 180	84.62 422

FMVSNet5		69.50 392	67.96 389	74.15 404	82.97 373	55.35 416	80.01 387	82.12 386	62.56 397	63.02 445	81.53 403	36.92 450	81.92 430	48.42 436	74.06 389	85.17 414

0.4-1-1-0.1		70.93 373	67.94 391	79.91 307	79.35 429	61.27 327	78.95 402	82.19 384	63.36 383	67.50 402	69.40 474	39.83 435	91.04 308	62.44 325	68.40 427	87.40 356

Syy-MVS		68.05 405	67.85 392	68.67 445	84.68 325	40.97 488	78.62 406	73.08 459	66.65 335	66.74 415	79.46 426	52.11 314	82.30 427	32.89 479	76.38 357	82.75 443

PatchT		68.46 403	67.85 392	70.29 436	80.70 409	43.93 480	72.47 451	74.88 451	60.15 418	70.55 360	76.57 448	49.94 350	81.59 431	50.58 421	74.83 383	85.34 409

pmmvs-eth3d		70.50 380	67.83 394	78.52 345	77.37 449	66.18 197	81.82 351	81.51 392	58.90 430	63.90 443	80.42 414	42.69 415	86.28 390	58.56 369	65.30 447	83.11 438

Anonymous20231206		68.60 399	67.80 395	71.02 433	80.23 415	50.75 456	78.30 413	80.47 406	56.79 447	66.11 425	82.63 391	46.35 385	78.95 444	43.62 459	75.70 364	83.36 435

Patchmatch-RL test		70.24 383	67.78 396	77.61 364	77.43 448	59.57 358	71.16 456	70.33 464	62.94 390	68.65 386	72.77 467	50.62 340	85.49 400	69.58 257	66.58 435	87.77 346

test0.0.03 1		68.00 406	67.69 397	68.90 442	77.55 447	47.43 465	75.70 435	72.95 461	66.66 332	66.56 417	82.29 396	48.06 369	75.87 465	44.97 457	74.51 386	83.41 434

testing3		68.56 401	67.67 398	71.22 432	87.33 248	42.87 482	83.06 338	71.54 462	70.36 260	69.08 383	84.38 349	30.33 468	85.69 397	37.50 474	75.45 372	85.09 416

EU-MVSNet		68.53 402	67.61 399	71.31 431	78.51 435	47.01 469	84.47 295	84.27 349	42.27 478	66.44 422	84.79 343	40.44 430	83.76 414	58.76 368	68.54 426	83.17 436

KD-MVS_self_test		68.81 397	67.59 400	72.46 422	74.29 462	45.45 472	77.93 417	87.00 304	63.12 385	63.99 442	78.99 433	42.32 417	84.77 408	56.55 392	64.09 450	87.16 371

test_fmvs2		68.35 404	67.48 401	70.98 434	69.50 480	51.95 443	80.05 386	76.38 445	49.33 469	74.65 313	84.38 349	23.30 481	75.40 470	74.51 198	75.17 380	85.60 404

tt0320-xc		70.11 385	67.45 402	78.07 354	85.33 308	59.51 359	83.28 329	78.96 426	58.77 431	67.10 410	80.28 417	36.73 451	87.42 379	56.83 389	59.77 464	87.29 364

mvs5depth		69.45 393	67.45 402	75.46 388	73.93 463	55.83 409	79.19 397	83.23 365	66.89 327	71.63 353	83.32 377	33.69 460	85.09 404	59.81 355	55.34 472	85.46 407

ppachtmachnet_test		70.04 386	67.34 404	78.14 351	79.80 422	61.13 328	79.19 397	80.59 403	59.16 427	65.27 431	79.29 428	46.75 380	87.29 380	49.33 432	66.72 433	86.00 399

Anonymous20240521		68.80 398	67.22 405	73.55 410	74.33 461	54.11 427	83.18 331	85.61 331	58.15 436	61.68 451	80.94 409	30.71 467	81.27 435	57.00 386	73.34 399	85.28 410

our_test_3		69.14 395	67.00 406	75.57 384	79.80 422	58.80 362	77.96 416	77.81 432	59.55 423	62.90 448	78.25 438	47.43 371	83.97 413	51.71 415	67.58 432	83.93 430

test20.03		67.45 408	66.95 407	68.94 441	75.48 458	44.84 478	77.50 421	77.67 433	66.66 332	63.01 446	83.80 365	47.02 375	78.40 446	42.53 465	68.86 425	83.58 433

0.4-1-1-0.2		70.01 388	66.86 408	79.44 325	77.61 446	60.64 343	76.77 427	82.34 382	62.40 399	65.91 426	66.65 476	40.05 432	90.83 317	61.77 339	68.24 428	86.86 379

0.3-1-1-0.015		70.03 387	66.80 409	79.72 317	78.18 440	61.07 331	77.63 420	82.32 383	62.65 396	65.50 428	67.29 475	37.62 449	90.91 315	61.99 335	68.04 429	87.19 368

MIMVSNet1		68.58 400	66.78 410	73.98 407	80.07 417	51.82 446	80.77 371	84.37 345	64.40 370	59.75 460	82.16 398	36.47 453	83.63 416	42.73 462	70.33 417	86.48 388

testgi		66.67 415	66.53 411	67.08 452	75.62 457	41.69 487	75.93 431	76.50 444	66.11 341	65.20 434	86.59 297	35.72 456	74.71 472	43.71 458	73.38 398	84.84 419

myMVS_eth3d		67.02 412	66.29 412	69.21 440	84.68 325	42.58 483	78.62 406	73.08 459	66.65 335	66.74 415	79.46 426	31.53 465	82.30 427	39.43 471	76.38 357	82.75 443

UnsupCasMVSNet_eth		67.33 409	65.99 413	71.37 428	73.48 468	51.47 450	75.16 439	85.19 335	65.20 357	60.78 454	80.93 411	42.35 416	77.20 452	57.12 383	53.69 474	85.44 408

dp		66.80 413	65.43 414	70.90 435	79.74 424	48.82 463	75.12 441	74.77 452	59.61 422	64.08 441	77.23 445	42.89 413	80.72 438	48.86 435	66.58 435	83.16 437

FE-MVSNET		67.25 411	65.33 415	73.02 417	75.86 454	52.54 440	80.26 384	80.56 404	63.80 381	60.39 455	79.70 425	41.41 424	84.66 410	43.34 460	62.62 455	81.86 450

UWE-MVS-28		65.32 422	64.93 416	66.49 453	78.70 433	38.55 490	77.86 419	64.39 482	62.00 405	64.13 440	83.60 372	41.44 423	76.00 463	31.39 481	80.89 293	84.92 417

TinyColmap		67.30 410	64.81 417	74.76 397	81.92 392	56.68 396	80.29 382	81.49 393	60.33 415	56.27 472	83.22 378	24.77 477	87.66 377	45.52 454	69.47 420	79.95 461

CHOSEN 280x420		66.51 416	64.71 418	71.90 424	81.45 399	63.52 281	57.98 488	68.95 471	53.57 458	62.59 449	76.70 447	46.22 387	75.29 471	55.25 396	79.68 309	76.88 468

TDRefinement		67.49 407	64.34 419	76.92 373	73.47 469	61.07 331	84.86 285	82.98 373	59.77 421	58.30 464	85.13 335	26.06 473	87.89 373	47.92 443	60.59 462	81.81 452

PM-MVS		66.41 417	64.14 420	73.20 415	73.92 464	56.45 398	78.97 401	64.96 481	63.88 380	64.72 435	80.24 418	19.84 485	83.44 420	66.24 285	64.52 449	79.71 462

dmvs_testset		62.63 431	64.11 421	58.19 463	78.55 434	24.76 501	75.28 437	65.94 478	67.91 319	60.34 456	76.01 456	53.56 298	73.94 477	31.79 480	67.65 431	75.88 470

KD-MVS_2432*1600		66.22 419	63.89 422	73.21 413	75.47 459	53.42 433	70.76 459	84.35 346	64.10 374	66.52 419	78.52 435	34.55 458	84.98 405	50.40 423	50.33 479	81.23 454

miper_refine_blended		66.22 419	63.89 422	73.21 413	75.47 459	53.42 433	70.76 459	84.35 346	64.10 374	66.52 419	78.52 435	34.55 458	84.98 405	50.40 423	50.33 479	81.23 454

MDA-MVSNet-bldmvs		66.68 414	63.66 424	75.75 381	79.28 430	60.56 345	73.92 448	78.35 430	64.43 368	50.13 480	79.87 423	44.02 407	83.67 415	46.10 451	56.86 466	83.03 440

ADS-MVSNet2		66.20 421	63.33 425	74.82 396	79.92 418	58.75 363	67.55 471	75.19 449	53.37 459	65.25 432	75.86 457	42.32 417	80.53 439	41.57 466	68.91 423	85.18 412

Patchmatch-test		64.82 425	63.24 426	69.57 438	79.42 428	49.82 460	63.49 485	69.05 470	51.98 464	59.95 459	80.13 419	50.91 336	70.98 480	40.66 468	73.57 394	87.90 343

MDA-MVSNet_test_wron		65.03 423	62.92 427	71.37 428	75.93 452	56.73 393	69.09 468	74.73 453	57.28 445	54.03 475	77.89 439	45.88 390	74.39 474	49.89 429	61.55 458	82.99 441

YYNet1		65.03 423	62.91 428	71.38 427	75.85 455	56.60 397	69.12 467	74.66 455	57.28 445	54.12 474	77.87 440	45.85 391	74.48 473	49.95 428	61.52 459	83.05 439

ADS-MVSNet		64.36 427	62.88 429	68.78 444	79.92 418	47.17 468	67.55 471	71.18 463	53.37 459	65.25 432	75.86 457	42.32 417	73.99 476	41.57 466	68.91 423	85.18 412

JIA-IIPM		66.32 418	62.82 430	76.82 374	77.09 450	61.72 320	65.34 479	75.38 448	58.04 439	64.51 437	62.32 480	42.05 421	86.51 387	51.45 418	69.22 422	82.21 447

LF4IMVS		64.02 428	62.19 431	69.50 439	70.90 477	53.29 436	76.13 429	77.18 440	52.65 461	58.59 462	80.98 408	23.55 480	76.52 457	53.06 410	66.66 434	78.68 464

test_fmvs3		63.36 430	61.82 432	67.98 449	62.51 489	46.96 470	77.37 423	74.03 456	45.24 474	67.50 402	78.79 434	12.16 493	72.98 479	72.77 219	66.02 437	83.99 429

new-patchmatchnet		61.73 433	61.73 433	61.70 459	72.74 474	24.50 502	69.16 466	78.03 431	61.40 408	56.72 469	75.53 460	38.42 443	76.48 458	45.95 452	57.67 465	84.13 427

usedtu_dtu_shiyan2		64.75 426	61.63 434	74.10 405	70.64 478	53.18 438	82.10 350	81.27 397	56.22 451	56.39 471	74.67 462	27.94 471	83.56 417	42.71 463	62.73 454	85.57 405

UnsupCasMVSNet_bld		63.70 429	61.53 435	70.21 437	73.69 466	51.39 451	72.82 450	81.89 387	55.63 453	57.81 466	71.80 469	38.67 442	78.61 445	49.26 433	52.21 477	80.63 458

mvsany_test1		62.30 432	61.26 436	65.41 455	69.52 479	54.86 421	66.86 473	49.78 495	46.65 472	68.50 391	83.21 379	49.15 362	66.28 487	56.93 387	60.77 460	75.11 471

PVSNet_0	57.27 20	61.67 434	59.27 437	68.85 443	79.61 425	57.44 385	68.01 469	73.44 458	55.93 452	58.54 463	70.41 472	44.58 402	77.55 451	47.01 445	35.91 487	71.55 475

test_vis1_rt		60.28 435	58.42 438	65.84 454	67.25 483	55.60 413	70.44 461	60.94 487	44.33 476	59.00 461	66.64 477	24.91 476	68.67 485	62.80 318	69.48 419	73.25 473

MVS-HIRNet		59.14 437	57.67 439	63.57 457	81.65 394	43.50 481	71.73 453	65.06 480	39.59 482	51.43 477	57.73 485	38.34 444	82.58 426	39.53 469	73.95 390	64.62 481

ttmdpeth		59.91 436	57.10 440	68.34 447	67.13 484	46.65 471	74.64 444	67.41 474	48.30 470	62.52 450	85.04 339	20.40 483	75.93 464	42.55 464	45.90 485	82.44 445

DSMNet-mixed		57.77 439	56.90 441	60.38 461	67.70 482	35.61 492	69.18 465	53.97 493	32.30 491	57.49 467	79.88 422	40.39 431	68.57 486	38.78 472	72.37 403	76.97 467

WB-MVS		54.94 441	54.72 442	55.60 469	73.50 467	20.90 503	74.27 447	61.19 486	59.16 427	50.61 478	74.15 463	47.19 374	75.78 466	17.31 493	35.07 488	70.12 476

pmmvs3		57.79 438	54.26 443	68.37 446	64.02 488	56.72 394	75.12 441	65.17 479	40.20 480	52.93 476	69.86 473	20.36 484	75.48 468	45.45 455	55.25 473	72.90 474

SSC-MVS		53.88 444	53.59 444	54.75 471	72.87 473	19.59 504	73.84 449	60.53 488	57.58 443	49.18 482	73.45 466	46.34 386	75.47 469	16.20 496	32.28 490	69.20 477

N_pmnet		52.79 447	53.26 445	51.40 473	78.99 432	7.68 507	69.52 463	3.89 506	51.63 465	57.01 468	74.98 461	40.83 428	65.96 488	37.78 473	64.67 448	80.56 460

MVStest1		56.63 440	52.76 446	68.25 448	61.67 490	53.25 437	71.67 454	68.90 472	38.59 483	50.59 479	83.05 382	25.08 475	70.66 481	36.76 475	38.56 486	80.83 457

FPMVS		53.68 445	51.64 447	59.81 462	65.08 486	51.03 453	69.48 464	69.58 468	41.46 479	40.67 486	72.32 468	16.46 489	70.00 484	24.24 489	65.42 446	58.40 486

mvsany_test3		53.99 443	51.45 448	61.61 460	55.51 494	44.74 479	63.52 484	45.41 499	43.69 477	58.11 465	76.45 449	17.99 486	63.76 490	54.77 400	47.59 481	76.34 469

test_f		52.09 448	50.82 449	55.90 467	53.82 497	42.31 486	59.42 487	58.31 491	36.45 486	56.12 473	70.96 471	12.18 492	57.79 493	53.51 407	56.57 468	67.60 478

new_pmnet		50.91 450	50.29 450	52.78 472	68.58 481	34.94 494	63.71 483	56.63 492	39.73 481	44.95 483	65.47 478	21.93 482	58.48 492	34.98 477	56.62 467	64.92 480

APD_test1		53.31 446	49.93 451	63.42 458	65.68 485	50.13 458	71.59 455	66.90 476	34.43 488	40.58 487	71.56 470	8.65 498	76.27 460	34.64 478	55.36 471	63.86 482

LCM-MVSNet		54.25 442	49.68 452	67.97 450	53.73 498	45.28 475	66.85 474	80.78 400	35.96 487	39.45 488	62.23 481	8.70 497	78.06 449	48.24 440	51.20 478	80.57 459

EGC-MVSNET		52.07 449	47.05 453	67.14 451	83.51 353	60.71 341	80.50 378	67.75 473	0.07 501	0.43 502	75.85 459	24.26 478	81.54 432	28.82 483	62.25 456	59.16 484

test_vis3_rt		49.26 452	47.02 454	56.00 466	54.30 495	45.27 476	66.76 475	48.08 496	36.83 485	44.38 484	53.20 489	7.17 500	64.07 489	56.77 390	55.66 469	58.65 485

ANet_high		50.57 451	46.10 455	63.99 456	48.67 501	39.13 489	70.99 458	80.85 399	61.39 409	31.18 490	57.70 486	17.02 488	73.65 478	31.22 482	15.89 498	79.18 463

dongtai		45.42 455	45.38 456	45.55 475	73.36 470	26.85 499	67.72 470	34.19 501	54.15 457	49.65 481	56.41 488	25.43 474	62.94 491	19.45 491	28.09 492	46.86 491

testf1		45.72 453	41.96 457	57.00 464	56.90 492	45.32 473	66.14 476	59.26 489	26.19 492	30.89 491	60.96 483	4.14 501	70.64 482	26.39 487	46.73 483	55.04 487

APD_test2		45.72 453	41.96 457	57.00 464	56.90 492	45.32 473	66.14 476	59.26 489	26.19 492	30.89 491	60.96 483	4.14 501	70.64 482	26.39 487	46.73 483	55.04 487

Gipuma		45.18 456	41.86 459	55.16 470	77.03 451	51.52 449	32.50 494	80.52 405	32.46 490	27.12 493	35.02 494	9.52 496	75.50 467	22.31 490	60.21 463	38.45 493
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
kuosan		39.70 459	40.40 460	37.58 478	64.52 487	26.98 497	65.62 478	33.02 502	46.12 473	42.79 485	48.99 491	24.10 479	46.56 499	12.16 499	26.30 493	39.20 492

PMVS	37.38 22	44.16 457	40.28 461	55.82 468	40.82 503	42.54 485	65.12 480	63.99 483	34.43 488	24.48 494	57.12 487	3.92 503	76.17 462	17.10 494	55.52 470	48.75 489
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 458	38.86 462	46.69 474	53.84 496	16.45 505	48.61 491	49.92 494	37.49 484	31.67 489	60.97 482	8.14 499	56.42 494	28.42 484	30.72 491	67.19 479

E-PMN		31.77 460	30.64 463	35.15 479	52.87 499	27.67 496	57.09 489	47.86 497	24.64 494	16.40 499	33.05 495	11.23 494	54.90 495	14.46 497	18.15 496	22.87 495

EMVS		30.81 462	29.65 464	34.27 480	50.96 500	25.95 500	56.58 490	46.80 498	24.01 495	15.53 500	30.68 496	12.47 491	54.43 496	12.81 498	17.05 497	22.43 496

test_method		31.52 461	29.28 465	38.23 477	27.03 505	6.50 508	20.94 496	62.21 485	4.05 499	22.35 497	52.50 490	13.33 490	47.58 497	27.04 486	34.04 489	60.62 483

cdsmvs_eth3d_5k		19.96 464	26.61 466	0.00 486	0.00 509	0.00 511	0.00 497	89.26 226	0.00 504	0.00 505	88.61 236	61.62 210	0.00 505	0.00 503	0.00 503	0.00 501

MVE	26.22 23	30.37 463	25.89 467	43.81 476	44.55 502	35.46 493	28.87 495	39.07 500	18.20 496	18.58 498	40.18 493	2.68 504	47.37 498	17.07 495	23.78 495	48.60 490
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt		18.61 465	21.40 468	10.23 483	4.82 506	10.11 506	34.70 493	30.74 504	1.48 500	23.91 496	26.07 497	28.42 470	13.41 502	27.12 485	15.35 499	7.17 497

wuyk23d		16.82 466	15.94 469	19.46 482	58.74 491	31.45 495	39.22 492	3.74 507	6.84 498	6.04 501	2.70 501	1.27 505	24.29 501	10.54 500	14.40 500	2.63 498

ab-mvs-re		7.23 467	9.64 470	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	86.72 289	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

test123		6.12 468	8.11 471	0.14 484	0.06 508	0.09 509	71.05 457	0.03 509	0.04 503	0.25 504	1.30 503	0.05 506	0.03 504	0.21 502	0.01 502	0.29 499

testmvs		6.04 469	8.02 472	0.10 485	0.08 507	0.03 510	69.74 462	0.04 508	0.05 502	0.31 503	1.68 502	0.02 507	0.04 503	0.24 501	0.02 501	0.25 500

pcd_1.5k_mvsjas		5.26 470	7.02 473	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	63.15 181	0.00 505	0.00 503	0.00 503	0.00 501

mmdepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

monomultidepth		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

test_blank		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet_test		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

DCPMVS		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

sosnet-low-res		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

sosnet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uncertanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

Regformer		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

uanet		0.00 471	0.00 474	0.00 486	0.00 509	0.00 511	0.00 497	0.00 510	0.00 504	0.00 505	0.00 504	0.00 508	0.00 505	0.00 503	0.00 503	0.00 501

MED-MVS test				87.86 27	94.57 17	71.43 61	93.28 12	94.36 3	75.24 129	92.25 9	95.03 22		97.39 11	88.15 39	95.96 19	94.75 33

TestfortrainingZip				87.28 46	92.85 68	72.05 50	93.28 12	93.32 37	76.52 88	88.91 32	93.52 77	77.30 17	96.67 33		91.98 94	93.13 139

WAC-MVS						42.58 483								39.46 470

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 155	91.30 17

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 55					97.53 2	89.67 15	96.44 9	94.41 59

PC_three_1452									68.21 316	92.02 14	94.00 63	82.09 5	95.98 62	84.58 71	96.68 2	94.95 13

No_MVS				89.16 1	94.34 31	75.53 2		92.99 55					97.53 2	89.67 15	96.44 9	94.41 59

test_one_0601					95.07 7	71.46 60		94.14 9	78.27 41	92.05 13	95.74 8	80.83 12

eth-test2					0.00 509

eth-test					0.00 509

ZD-MVS					94.38 29	72.22 46		92.67 73	70.98 242	87.75 51	94.07 58	74.01 37	96.70 31	84.66 70	94.84 47

IU-MVS					95.30 2	71.25 65		92.95 61	66.81 328	92.39 6				88.94 28	96.63 4	94.85 22

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 17

test_241102_TWO								94.06 14	77.24 63	92.78 4	95.72 10	81.26 9	97.44 7	89.07 25	96.58 6	94.26 71

test_241102_ONE					95.30 2	70.98 73		94.06 14	77.17 66	93.10 1	95.39 18	82.99 1	97.27 14

save fliter					93.80 44	72.35 44	90.47 74	91.17 152	74.31 161

test_0728_THIRD									78.38 38	92.12 11	95.78 6	81.46 8	97.40 9	89.42 19	96.57 7	94.67 40

test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 72

test0726					95.27 5	71.25 65	93.60 7	94.11 10	77.33 58	92.81 3	95.79 5	80.98 10

GSMVS																88.96 311

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 329				88.96 311

sam_mvs												50.01 348

ambc				75.24 391	73.16 471	50.51 457	63.05 486	87.47 288		64.28 438	77.81 441	17.80 487	89.73 340	57.88 377	60.64 461	85.49 406

MTGPA								92.02 112

test_post1							78.90 404				5.43 500	48.81 368	85.44 402	59.25 361

test_post											5.46 499	50.36 344	84.24 411

patchmatchnet-post											74.00 464	51.12 335	88.60 363

GG-mvs-BLEND				75.38 389	81.59 396	55.80 410	79.32 394	69.63 467		67.19 408	73.67 465	43.24 411	88.90 359	50.41 422	84.50 237	81.45 453

MTMP							92.18 39	32.83 503

gm-plane-assit					81.40 400	53.83 430			62.72 395		80.94 409		92.39 243	63.40 309

test9_res														84.90 64	95.70 29	92.87 154

TEST9					93.26 56	72.96 25	88.75 138	91.89 120	68.44 313	85.00 81	93.10 89	74.36 33	95.41 81

test_8					93.13 60	72.57 35	88.68 144	91.84 124	68.69 308	84.87 85	93.10 89	74.43 31	95.16 91

agg_prior2														82.91 91	95.45 32	92.70 159

agg_prior					92.85 68	71.94 53		91.78 128		84.41 96			94.93 102

TestCases				79.58 322	85.15 313	63.62 272		79.83 416	62.31 400	60.32 457	86.73 287	32.02 462	88.96 357	50.28 425	71.57 411	86.15 393

test_prior4						72.60 34	89.01 125

test_prior2							88.85 132		75.41 124	84.91 83	93.54 76	74.28 34		83.31 85	95.86 23

test_prior				86.33 65	92.61 75	69.59 99		92.97 60					95.48 75			93.91 88

旧先验2							86.56 231		58.10 438	87.04 62			88.98 355	74.07 203

新几何2							86.29 245

新几何1				83.42 195	93.13 60	70.71 81		85.48 333	57.43 444	81.80 152	91.98 121	63.28 175	92.27 249	64.60 301	92.99 76	87.27 365

旧先验1					91.96 81	65.79 211		86.37 320			93.08 93	69.31 100			92.74 80	88.74 322

无先验							87.48 188	88.98 241	60.00 419				94.12 142	67.28 278		88.97 310

原ACMM2							86.86 218

原ACMM1				84.35 142	93.01 66	68.79 118		92.44 83	63.96 379	81.09 165	91.57 141	66.06 151	95.45 76	67.19 280	94.82 49	88.81 317

test222					91.50 87	68.26 138	84.16 309	83.20 368	54.63 456	79.74 187	91.63 137	58.97 246			91.42 104	86.77 382

testdata2													91.01 309	62.37 329

segment_acmp												73.08 44

testdata				79.97 306	90.90 99	64.21 260		84.71 341	59.27 426	85.40 76	92.91 95	62.02 203	89.08 353	68.95 263	91.37 106	86.63 387

testdata1							84.14 310		75.71 115

test12				86.80 59	92.63 74	70.70 82		91.79 127		82.71 139		71.67 64	96.16 53		94.50 56	93.54 117

plane_prior7					90.08 117	68.51 132

plane_prior6					89.84 126	68.70 126						60.42 236

plane_prior5								92.44 83					95.38 83	78.71 144	86.32 205	91.33 214

plane_prior4											91.00 164

plane_prior3						68.60 129			78.44 36	78.92 202

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 125

plane_prior						68.71 124	90.38 78		77.62 47						86.16 210

n2								0.00 510

nn								0.00 510

door-mid								69.98 466

lessismore_v0				78.97 333	81.01 407	57.15 388		65.99 477		61.16 453	82.82 388	39.12 439	91.34 293	59.67 356	46.92 482	88.43 330

LGP-MVS_train				84.50 132	89.23 154	68.76 120		91.94 118	75.37 126	76.64 258	91.51 143	54.29 290	94.91 103	78.44 146	83.78 250	89.83 282

test11								92.23 98

door								69.44 469

HQP5-MVS						66.98 185

HQP-NCC					89.33 146		89.17 116		76.41 94	77.23 243

ACMP_Plane					89.33 146		89.17 116		76.41 94	77.23 243

BP-MVS														77.47 159

HQP4-MVS										77.24 242			95.11 95			91.03 224

HQP3-MVS								92.19 106							85.99 214

HQP2-MVS												60.17 239

NP-MVS					89.62 131	68.32 136					90.24 186

MDTV_nov1_ep13_2view						37.79 491	75.16 439		55.10 454	66.53 418		49.34 359		53.98 404		87.94 342

ACMMP++_ref															81.95 283

ACMMP++															81.25 288

Test By Simon												64.33 167

ITE_SJBPF				78.22 349	81.77 393	60.57 344		83.30 363	69.25 291	67.54 401	87.20 278	36.33 454	87.28 381	54.34 402	74.62 385	86.80 381

DeepMVS_CX				27.40 481	40.17 504	26.90 498		24.59 505	17.44 497	23.95 495	48.61 492	9.77 495	26.48 500	18.06 492	24.47 494	28.83 494

High-resolution multi-view benchmark