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.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 54	96.26 30	72.84 26	99.38 1	92.64 19	95.93 9	97.08 10

MM		90.87 2	91.52 2	88.92 14	92.12 95	71.10 28	97.02 3	96.04 6	88.70 2	91.57 13	96.19 33	70.12 40	98.91 17	96.83 1	95.06 16	96.76 14

DPM-MVS		90.70 3	90.52 8	91.24 1	89.68 153	76.68 2	97.29 1	95.35 15	82.87 21	91.58 12	97.22 3	79.93 5	99.10 9	83.12 97	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 15	97.31 4	69.91 43	93.96 70	94.37 52	72.48 182	92.07 8	96.85 16	83.82 2	99.15 2	91.53 29	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 88	92.83 76	64.03 191	93.06 111	94.33 54	82.19 28	93.65 3	96.15 35	85.89 1	97.19 83	91.02 33	97.75 1	96.43 29
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
CNVR-MVS		90.32 6	90.89 7	88.61 21	96.76 8	70.65 32	96.47 13	94.83 30	84.83 12	89.07 32	96.80 19	70.86 36	99.06 15	92.64 19	95.71 10	96.12 38

DELS-MVS		90.05 7	90.09 11	89.94 4	93.14 69	73.88 9	97.01 4	94.40 50	88.32 3	85.71 55	94.91 70	74.11 19	98.91 17	87.26 61	95.94 8	97.03 11
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
MVS_0304		90.01 8	90.50 9	88.53 22	90.14 144	70.94 29	96.47 13	95.72 10	87.33 4	89.60 29	96.26 30	68.44 46	98.74 24	95.82 4	94.72 30	95.90 45

SED-MVS		89.94 9	90.36 10	88.70 17	96.45 12	69.38 54	96.89 5	94.44 46	71.65 212	92.11 6	97.21 4	76.79 9	99.11 6	92.34 21	95.36 13	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 132	93.00 73	58.16 305	96.72 8	94.41 48	86.50 8	90.25 22	97.83 1	75.46 14	98.67 25	92.78 18	95.49 12	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 91	96.04 24	63.70 201	95.04 40	95.19 19	86.74 7	91.53 14	95.15 63	73.86 20	97.58 59	93.38 14	92.00 70	96.28 35

CANet		89.61 12	89.99 12	88.46 23	94.39 39	69.71 50	96.53 12	93.78 66	86.89 6	89.68 28	95.78 40	65.94 69	99.10 9	92.99 16	93.91 41	96.58 20

DVP-MVS		89.41 13	89.73 14	88.45 24	96.40 15	69.99 39	96.64 9	94.52 42	71.92 198	90.55 19	96.93 11	73.77 21	99.08 11	91.91 27	94.90 21	96.29 33
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
HPM-MVS++		89.37 14	89.95 13	87.64 33	95.10 30	68.23 84	95.24 33	94.49 44	82.43 25	88.90 33	96.35 27	71.89 33	98.63 26	88.76 48	96.40 6	96.06 39

NCCC		89.07 15	89.46 15	87.91 27	96.60 10	69.05 62	96.38 15	94.64 39	84.42 13	86.74 46	96.20 32	66.56 65	98.76 23	89.03 47	94.56 32	95.92 44

MVSMamba_pp		88.94 16	88.82 17	89.29 12	94.04 45	74.01 7	94.81 46	92.74 111	85.13 10	90.37 21	90.13 179	68.40 48	97.38 70	89.42 39	94.34 35	96.47 27

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 100	94.17 58	94.15 59	68.77 262	90.74 17	97.27 2	76.09 12	98.49 29	90.58 37	94.91 20	96.30 32
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		88.14 18	88.29 22	87.67 32	93.21 66	68.72 70	93.85 77	94.03 62	74.18 145	91.74 11	96.67 21	65.61 73	98.42 33	89.24 44	96.08 7	95.88 46
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
PS-MVSNAJ		88.14 18	87.61 29	89.71 6	92.06 96	76.72 1	95.75 20	93.26 90	83.86 15	89.55 30	96.06 36	53.55 219	97.89 43	91.10 31	93.31 53	94.54 105

TSAR-MVS + MP.		88.11 20	88.64 18	86.54 70	91.73 109	68.04 88	90.36 225	93.55 79	82.89 20	91.29 15	92.89 121	72.27 30	96.03 143	87.99 52	94.77 25	95.54 55
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + GP.		87.96 21	88.37 21	86.70 63	93.51 60	65.32 156	95.15 36	93.84 65	78.17 91	85.93 53	94.80 73	75.80 13	98.21 34	89.38 41	88.78 104	96.59 18

DeepC-MVS_fast	79.48 2	87.95 22	88.00 25	87.79 30	95.86 27	68.32 78	95.74 21	94.11 60	83.82 16	83.49 76	96.19 33	64.53 87	98.44 31	83.42 96	94.88 24	96.61 17
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xiu_mvs_v2_base		87.92 23	87.38 33	89.55 11	91.41 120	76.43 3	95.74 21	93.12 98	83.53 18	89.55 30	95.95 38	53.45 223	97.68 50	91.07 32	92.62 60	94.54 105

EPNet		87.84 24	88.38 20	86.23 80	93.30 63	66.05 138	95.26 32	94.84 29	87.09 5	88.06 35	94.53 79	66.79 62	97.34 74	83.89 93	91.68 75	95.29 67
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 25	87.77 27	87.63 37	89.24 168	71.18 25	96.57 11	92.90 106	82.70 23	87.13 41	95.27 56	64.99 78	95.80 148	89.34 42	91.80 73	95.93 43

test_fmvsm_n_1920		87.69 26	88.50 19	85.27 112	87.05 224	63.55 208	93.69 87	91.08 186	84.18 14	90.17 24	97.04 8	67.58 56	97.99 39	95.72 5	90.03 95	94.26 113

APDe-MVS		87.54 27	87.84 26	86.65 64	96.07 23	66.30 134	94.84 45	93.78 66	69.35 253	88.39 34	96.34 28	67.74 55	97.66 54	90.62 36	93.44 51	96.01 42
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_l_conf0.5_n		87.49 28	88.19 23	85.39 106	86.95 225	64.37 181	94.30 55	88.45 289	80.51 49	92.70 4	96.86 15	69.98 41	97.15 87	95.83 3	88.08 112	94.65 99

SD-MVS		87.49 28	87.49 31	87.50 42	93.60 55	68.82 68	93.90 74	92.63 118	76.86 110	87.90 36	95.76 41	66.17 66	97.63 56	89.06 46	91.48 79	96.05 40
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
fmvsm_l_conf0.5_n_a		87.44 30	88.15 24	85.30 110	87.10 222	64.19 188	94.41 53	88.14 298	80.24 57	92.54 5	96.97 10	69.52 43	97.17 84	95.89 2	88.51 107	94.56 102

dcpmvs_2		87.37 31	87.55 30	86.85 56	95.04 32	68.20 85	90.36 225	90.66 198	79.37 71	81.20 93	93.67 105	74.73 15	96.55 121	90.88 34	92.00 70	95.82 47

alignmvs		87.28 32	86.97 37	88.24 26	91.30 122	71.14 27	95.61 25	93.56 78	79.30 72	87.07 43	95.25 58	68.43 47	96.93 107	87.87 53	84.33 147	96.65 16

train_agg		87.21 33	87.42 32	86.60 66	94.18 41	67.28 107	94.16 59	93.51 80	71.87 203	85.52 57	95.33 51	68.19 50	97.27 81	89.09 45	94.90 21	95.25 73

MG-MVS		87.11 34	86.27 44	89.62 7	97.79 1	76.27 4	94.96 43	94.49 44	78.74 86	83.87 75	92.94 119	64.34 88	96.94 105	75.19 154	94.09 37	95.66 50

SF-MVS		87.03 35	87.09 35	86.84 57	92.70 82	67.45 105	93.64 90	93.76 69	70.78 237	86.25 48	96.44 26	66.98 60	97.79 47	88.68 49	94.56 32	95.28 69

CSCG		86.87 36	86.26 45	88.72 16	95.05 31	70.79 31	93.83 82	95.33 16	68.48 266	77.63 139	94.35 88	73.04 24	98.45 30	84.92 83	93.71 46	96.92 13

sasdasda		86.85 37	86.25 46	88.66 19	91.80 107	71.92 16	93.54 95	91.71 155	80.26 54	87.55 38	95.25 58	63.59 101	96.93 107	88.18 50	84.34 145	97.11 8

canonicalmvs		86.85 37	86.25 46	88.66 19	91.80 107	71.92 16	93.54 95	91.71 155	80.26 54	87.55 38	95.25 58	63.59 101	96.93 107	88.18 50	84.34 145	97.11 8

PHI-MVS		86.83 39	86.85 41	86.78 61	93.47 61	65.55 152	95.39 30	95.10 22	71.77 208	85.69 56	96.52 23	62.07 121	98.77 22	86.06 73	95.60 11	96.03 41

SteuartSystems-ACMMP		86.82 40	86.90 39	86.58 68	90.42 137	66.38 131	96.09 17	93.87 64	77.73 98	84.01 74	95.66 43	63.39 104	97.94 40	87.40 59	93.55 49	95.42 56
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 41	86.86 40	86.31 79	93.76 50	67.53 102	96.33 16	93.61 76	82.34 27	81.00 98	93.08 115	63.19 108	97.29 77	87.08 64	91.38 81	94.13 120

testing11		86.71 42	86.44 43	87.55 40	93.54 58	71.35 22	93.65 89	95.58 11	81.36 41	80.69 101	92.21 139	72.30 29	96.46 126	85.18 79	83.43 154	94.82 92

test_fmvsmconf_n		86.58 43	87.17 34	84.82 125	85.28 255	62.55 232	94.26 57	89.78 232	83.81 17	87.78 37	96.33 29	65.33 75	96.98 99	94.40 11	87.55 117	94.95 83

jason		86.40 44	86.17 48	87.11 50	86.16 240	70.54 34	95.71 24	92.19 133	82.00 30	84.58 67	94.34 89	61.86 123	95.53 169	87.76 54	90.89 87	95.27 70
jason: jason.
fmvsm_s_conf0.5_n		86.39 45	86.91 38	84.82 125	87.36 217	63.54 209	94.74 48	90.02 226	82.52 24	90.14 25	96.92 13	62.93 113	97.84 46	95.28 8	82.26 164	93.07 156

WTY-MVS		86.32 46	85.81 55	87.85 28	92.82 78	69.37 56	95.20 34	95.25 17	82.71 22	81.91 87	94.73 74	67.93 54	97.63 56	79.55 123	82.25 165	96.54 21

MSLP-MVS++		86.27 47	85.91 54	87.35 45	92.01 99	68.97 65	95.04 40	92.70 112	79.04 81	81.50 90	96.50 25	58.98 156	96.78 113	83.49 95	93.93 40	96.29 33

VNet		86.20 48	85.65 59	87.84 29	93.92 47	69.99 39	95.73 23	95.94 7	78.43 88	86.00 52	93.07 116	58.22 162	97.00 95	85.22 77	84.33 147	96.52 22

MVS_111021_HR		86.19 49	85.80 56	87.37 44	93.17 68	69.79 47	93.99 69	93.76 69	79.08 79	78.88 127	93.99 99	62.25 120	98.15 36	85.93 74	91.15 85	94.15 119

CS-MVS-test		86.14 50	87.01 36	83.52 170	92.63 84	59.36 293	95.49 27	91.92 142	80.09 58	85.46 59	95.53 47	61.82 125	95.77 151	86.77 68	93.37 52	95.41 57

ACMMP_NAP		86.05 51	85.80 56	86.80 60	91.58 113	67.53 102	91.79 169	93.49 83	74.93 136	84.61 66	95.30 53	59.42 149	97.92 41	86.13 71	94.92 19	94.94 84

testing99		86.01 52	85.47 60	87.63 37	93.62 54	71.25 24	93.47 101	95.23 18	80.42 52	80.60 103	91.95 143	71.73 34	96.50 124	80.02 120	82.22 166	95.13 76

ETV-MVS		86.01 52	86.11 49	85.70 98	90.21 142	67.02 116	93.43 103	91.92 142	81.21 43	84.13 73	94.07 98	60.93 133	95.63 159	89.28 43	89.81 96	94.46 111

testing91		85.93 54	85.31 63	87.78 31	93.59 56	71.47 19	93.50 98	95.08 25	80.26 54	80.53 104	91.93 144	70.43 38	96.51 123	80.32 118	82.13 168	95.37 60

APD-MVS		85.93 54	85.99 52	85.76 95	95.98 26	65.21 159	93.59 93	92.58 120	66.54 279	86.17 50	95.88 39	63.83 94	97.00 95	86.39 70	92.94 57	95.06 78
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 56	85.46 61	87.18 48	88.20 196	72.42 15	92.41 141	92.77 109	82.11 29	80.34 107	93.07 116	68.27 49	95.02 182	78.39 135	93.59 48	94.09 122

CS-MVS		85.80 57	86.65 42	83.27 178	92.00 100	58.92 298	95.31 31	91.86 147	79.97 59	84.82 65	95.40 49	62.26 119	95.51 170	86.11 72	92.08 69	95.37 60

fmvsm_s_conf0.5_n_a		85.75 58	86.09 50	84.72 132	85.73 249	63.58 206	93.79 83	89.32 250	81.42 39	90.21 23	96.91 14	62.41 118	97.67 51	94.48 10	80.56 183	92.90 162

test_fmvsmconf0.1_n		85.71 59	86.08 51	84.62 139	80.83 309	62.33 237	93.84 80	88.81 276	83.50 19	87.00 44	96.01 37	63.36 105	96.93 107	94.04 12	87.29 120	94.61 101

CDPH-MVS		85.71 59	85.46 61	86.46 72	94.75 34	67.19 109	93.89 75	92.83 108	70.90 233	83.09 79	95.28 54	63.62 99	97.36 72	80.63 115	94.18 36	94.84 89

casdiffmvs_mvg		85.66 61	85.18 65	87.09 51	88.22 195	69.35 57	93.74 86	91.89 145	81.47 35	80.10 109	91.45 153	64.80 83	96.35 127	87.23 62	87.69 115	95.58 53
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_conf0.1_n		85.61 62	85.93 53	84.68 135	82.95 293	63.48 211	94.03 68	89.46 244	81.69 33	89.86 26	96.74 20	61.85 124	97.75 49	94.74 9	82.01 170	92.81 164

MGCFI-Net		85.59 63	85.73 58	85.17 116	91.41 120	62.44 233	92.87 119	91.31 172	79.65 65	86.99 45	95.14 64	62.90 114	96.12 135	87.13 63	84.13 152	96.96 12

DeepC-MVS	77.85 3	85.52 64	85.24 64	86.37 76	88.80 178	66.64 124	92.15 148	93.68 74	81.07 44	76.91 149	93.64 106	62.59 116	98.44 31	85.50 75	92.84 59	94.03 126
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.37 65	84.87 71	86.84 57	88.25 193	69.07 61	93.04 113	91.76 152	81.27 42	80.84 100	92.07 141	64.23 89	96.06 141	84.98 82	87.43 119	95.39 58
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 66	85.08 67	86.06 83	93.09 71	65.65 148	93.89 75	93.41 87	73.75 156	79.94 111	94.68 76	60.61 136	98.03 38	82.63 100	93.72 45	94.52 107

MP-MVS-pluss		85.24 67	85.13 66	85.56 101	91.42 118	65.59 150	91.54 179	92.51 122	74.56 139	80.62 102	95.64 44	59.15 153	97.00 95	86.94 66	93.80 42	94.07 124
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 68	84.69 73	86.63 65	92.91 75	69.91 43	92.61 132	95.80 9	80.31 53	80.38 106	92.27 136	68.73 45	95.19 179	75.94 149	83.27 156	94.81 93

PAPR		85.15 69	84.47 74	87.18 48	96.02 25	68.29 79	91.85 167	93.00 103	76.59 117	79.03 123	95.00 65	61.59 126	97.61 58	78.16 136	89.00 103	95.63 51

MP-MVS		85.02 70	84.97 69	85.17 116	92.60 85	64.27 186	93.24 106	92.27 127	73.13 167	79.63 115	94.43 82	61.90 122	97.17 84	85.00 81	92.56 61	94.06 125
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 71	84.44 75	86.71 62	88.33 190	68.73 69	90.24 230	91.82 151	81.05 45	81.18 94	92.50 128	63.69 97	96.08 140	84.45 88	86.71 129	95.32 65

CHOSEN 1792x2688		84.98 72	83.45 88	89.57 10	89.94 148	75.14 5	92.07 154	92.32 125	81.87 31	75.68 158	88.27 202	60.18 139	98.60 27	80.46 117	90.27 94	94.96 82

EIA-MVS		84.84 73	84.88 70	84.69 134	91.30 122	62.36 236	93.85 77	92.04 137	79.45 68	79.33 120	94.28 92	62.42 117	96.35 127	80.05 119	91.25 84	95.38 59

fmvsm_s_conf0.1_n_a		84.76 74	84.84 72	84.53 141	80.23 319	63.50 210	92.79 121	88.73 279	80.46 50	89.84 27	96.65 22	60.96 132	97.57 61	93.80 13	80.14 185	92.53 172

HFP-MVS		84.73 75	84.40 76	85.72 97	93.75 52	65.01 165	93.50 98	93.19 94	72.19 192	79.22 121	94.93 68	59.04 154	97.67 51	81.55 106	92.21 65	94.49 110

MVS		84.66 76	82.86 104	90.06 2	90.93 128	74.56 6	87.91 275	95.54 13	68.55 264	72.35 200	94.71 75	59.78 145	98.90 19	81.29 112	94.69 31	96.74 15

GST-MVS		84.63 77	84.29 77	85.66 99	92.82 78	65.27 157	93.04 113	93.13 97	73.20 165	78.89 124	94.18 95	59.41 150	97.85 45	81.45 108	92.48 63	93.86 134

EC-MVSNet		84.53 78	85.04 68	83.01 182	89.34 160	61.37 257	94.42 52	91.09 184	77.91 95	83.24 77	94.20 94	58.37 160	95.40 171	85.35 76	91.41 80	92.27 182

ACMMPR		84.37 79	84.06 78	85.28 111	93.56 57	64.37 181	93.50 98	93.15 96	72.19 192	78.85 129	94.86 71	56.69 182	97.45 65	81.55 106	92.20 66	94.02 127

region2R		84.36 80	84.03 79	85.36 108	93.54 58	64.31 184	93.43 103	92.95 104	72.16 195	78.86 128	94.84 72	56.97 177	97.53 63	81.38 110	92.11 68	94.24 114

LFMVS		84.34 81	82.73 106	89.18 13	94.76 33	73.25 11	94.99 42	91.89 145	71.90 200	82.16 86	93.49 110	47.98 270	97.05 90	82.55 101	84.82 141	97.25 7

test_yl		84.28 82	83.16 97	87.64 33	94.52 37	69.24 58	95.78 18	95.09 23	69.19 256	81.09 95	92.88 122	57.00 175	97.44 66	81.11 113	81.76 172	96.23 36

DCV-MVSNet		84.28 82	83.16 97	87.64 33	94.52 37	69.24 58	95.78 18	95.09 23	69.19 256	81.09 95	92.88 122	57.00 175	97.44 66	81.11 113	81.76 172	96.23 36

diffmvs		84.28 82	83.83 80	85.61 100	87.40 215	68.02 89	90.88 209	89.24 253	80.54 48	81.64 89	92.52 127	59.83 144	94.52 205	87.32 60	85.11 139	94.29 112
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
HY-MVS	76.49 5	84.28 82	83.36 95	87.02 54	92.22 92	67.74 95	84.65 301	94.50 43	79.15 76	82.23 85	87.93 211	66.88 61	96.94 105	80.53 116	82.20 167	96.39 31

ETVMVS		84.22 86	83.71 81	85.76 95	92.58 86	68.25 83	92.45 140	95.53 14	79.54 67	79.46 117	91.64 151	70.29 39	94.18 218	69.16 209	82.76 162	94.84 89

MAR-MVS		84.18 87	83.43 89	86.44 73	96.25 21	65.93 143	94.28 56	94.27 56	74.41 140	79.16 122	95.61 45	53.99 214	98.88 21	69.62 203	93.26 54	94.50 109
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
MVS_Test		84.16 88	83.20 96	87.05 53	91.56 114	69.82 46	89.99 239	92.05 136	77.77 97	82.84 80	86.57 231	63.93 93	96.09 137	74.91 159	89.18 102	95.25 73

CANet_DTU		84.09 89	83.52 83	85.81 92	90.30 140	66.82 119	91.87 165	89.01 268	85.27 9	86.09 51	93.74 103	47.71 274	96.98 99	77.90 138	89.78 98	93.65 139

iter_conf05_11		84.06 90	83.37 94	86.15 82	93.04 72	66.63 125	87.84 277	90.21 218	71.10 229	81.47 91	89.48 187	68.80 44	96.96 102	75.97 148	92.39 64	94.87 85

ET-MVSNet_ETH3D		84.01 91	83.15 99	86.58 68	90.78 133	70.89 30	94.74 48	94.62 40	81.44 38	58.19 329	93.64 106	73.64 23	92.35 282	82.66 99	78.66 200	96.50 26

PVSNet_Blended_VisFu		83.97 92	83.50 85	85.39 106	90.02 146	66.59 128	93.77 84	91.73 153	77.43 106	77.08 148	89.81 184	63.77 96	96.97 101	79.67 122	88.21 110	92.60 168

MTAPA		83.91 93	83.38 93	85.50 102	91.89 105	65.16 161	81.75 325	92.23 128	75.32 131	80.53 104	95.21 61	56.06 191	97.16 86	84.86 84	92.55 62	94.18 116

XVS		83.87 94	83.47 87	85.05 118	93.22 64	63.78 195	92.92 117	92.66 115	73.99 148	78.18 133	94.31 91	55.25 197	97.41 68	79.16 126	91.58 77	93.95 129

Effi-MVS+		83.82 95	82.76 105	86.99 55	89.56 156	69.40 53	91.35 191	86.12 322	72.59 179	83.22 78	92.81 125	59.60 147	96.01 145	81.76 105	87.80 114	95.56 54

test_fmvsmvis_n_1920		83.80 96	83.48 86	84.77 129	82.51 295	63.72 199	91.37 189	83.99 344	81.42 39	77.68 138	95.74 42	58.37 160	97.58 59	93.38 14	86.87 123	93.00 159

EI-MVSNet-Vis-set		83.77 97	83.67 82	84.06 156	92.79 81	63.56 207	91.76 172	94.81 31	79.65 65	77.87 136	94.09 96	63.35 106	97.90 42	79.35 124	79.36 192	90.74 208

MVSFormer		83.75 98	82.88 103	86.37 76	89.24 168	71.18 25	89.07 257	90.69 195	65.80 284	87.13 41	94.34 89	64.99 78	92.67 269	72.83 171	91.80 73	95.27 70

CP-MVS		83.71 99	83.40 92	84.65 136	93.14 69	63.84 193	94.59 50	92.28 126	71.03 231	77.41 142	94.92 69	55.21 200	96.19 132	81.32 111	90.70 89	93.91 131

test_fmvsmconf0.01_n		83.70 100	83.52 83	84.25 153	75.26 362	61.72 251	92.17 147	87.24 311	82.36 26	84.91 64	95.41 48	55.60 195	96.83 112	92.85 17	85.87 135	94.21 115

baseline2		83.68 101	83.42 91	84.48 144	87.37 216	66.00 140	90.06 234	95.93 8	79.71 64	69.08 236	90.39 171	77.92 6	96.28 129	78.91 130	81.38 176	91.16 204

thisisatest0515		83.41 102	82.49 110	86.16 81	89.46 159	68.26 81	93.54 95	94.70 36	74.31 143	75.75 156	90.92 161	72.62 27	96.52 122	69.64 201	81.50 175	93.71 137

PVSNet_BlendedMVS		83.38 103	83.43 89	83.22 179	93.76 50	67.53 102	94.06 63	93.61 76	79.13 77	81.00 98	85.14 247	63.19 108	97.29 77	87.08 64	73.91 236	84.83 303

test2506		83.29 104	82.92 102	84.37 148	88.39 188	63.18 218	92.01 157	91.35 171	77.66 100	78.49 132	91.42 154	64.58 86	95.09 181	73.19 167	89.23 100	94.85 86

PGM-MVS		83.25 105	82.70 107	84.92 121	92.81 80	64.07 190	90.44 221	92.20 132	71.28 224	77.23 145	94.43 82	55.17 201	97.31 76	79.33 125	91.38 81	93.37 145

HPM-MVS		83.25 105	82.95 101	84.17 154	92.25 91	62.88 227	90.91 206	91.86 147	70.30 242	77.12 146	93.96 100	56.75 180	96.28 129	82.04 103	91.34 83	93.34 146
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
iter_conf05		83.18 107	81.72 120	87.58 39	90.17 143	73.92 8	83.37 312	88.63 285	62.18 314	73.79 180	87.64 216	71.47 35	96.28 129	84.69 85	93.54 50	92.54 170

EI-MVSNet-UG-set		83.14 108	82.96 100	83.67 168	92.28 90	63.19 217	91.38 188	94.68 37	79.22 74	76.60 151	93.75 102	62.64 115	97.76 48	78.07 137	78.01 203	90.05 217

VDD-MVS		83.06 109	81.81 119	86.81 59	90.86 131	67.70 96	95.40 29	91.50 166	75.46 128	81.78 88	92.34 135	40.09 312	97.13 88	86.85 67	82.04 169	95.60 52

h-mvs33		83.01 110	82.56 109	84.35 149	89.34 160	62.02 243	92.72 124	93.76 69	81.45 36	82.73 82	92.25 138	60.11 140	97.13 88	87.69 55	62.96 314	93.91 131

PAPM_NR		82.97 111	81.84 118	86.37 76	94.10 44	66.76 122	87.66 280	92.84 107	69.96 246	74.07 177	93.57 108	63.10 111	97.50 64	70.66 196	90.58 91	94.85 86

mPP-MVS		82.96 112	82.44 111	84.52 142	92.83 76	62.92 225	92.76 122	91.85 149	71.52 220	75.61 161	94.24 93	53.48 222	96.99 98	78.97 129	90.73 88	93.64 140

SR-MVS		82.81 113	82.58 108	83.50 173	93.35 62	61.16 260	92.23 146	91.28 176	64.48 293	81.27 92	95.28 54	53.71 218	95.86 147	82.87 98	88.77 105	93.49 143

DP-MVS Recon		82.73 114	81.65 121	85.98 85	97.31 4	67.06 113	95.15 36	91.99 139	69.08 259	76.50 153	93.89 101	54.48 209	98.20 35	70.76 194	85.66 137	92.69 165

CLD-MVS		82.73 114	82.35 113	83.86 160	87.90 203	67.65 98	95.45 28	92.18 134	85.06 11	72.58 193	92.27 136	52.46 230	95.78 149	84.18 89	79.06 195	88.16 244
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 116	82.38 112	83.73 164	89.25 165	59.58 288	92.24 145	94.89 28	77.96 93	79.86 112	92.38 133	56.70 181	97.05 90	77.26 141	80.86 180	94.55 103

3Dnovator	73.91 6	82.69 117	80.82 132	88.31 25	89.57 155	71.26 23	92.60 133	94.39 51	78.84 83	67.89 257	92.48 131	48.42 265	98.52 28	68.80 214	94.40 34	95.15 75

MVSTER		82.47 118	82.05 114	83.74 162	92.68 83	69.01 63	91.90 164	93.21 91	79.83 60	72.14 201	85.71 243	74.72 16	94.72 193	75.72 150	72.49 247	87.50 249

TESTMET0.1,1		82.41 119	81.98 117	83.72 165	88.08 197	63.74 197	92.70 126	93.77 68	79.30 72	77.61 140	87.57 218	58.19 163	94.08 222	73.91 165	86.68 130	93.33 148

CostFormer		82.33 120	81.15 125	85.86 90	89.01 173	68.46 75	82.39 322	93.01 101	75.59 126	80.25 108	81.57 289	72.03 32	94.96 185	79.06 128	77.48 211	94.16 118

API-MVS		82.28 121	80.53 139	87.54 41	96.13 22	70.59 33	93.63 91	91.04 190	65.72 286	75.45 163	92.83 124	56.11 190	98.89 20	64.10 258	89.75 99	93.15 152

IB-MVS	77.80 4	82.18 122	80.46 141	87.35 45	89.14 170	70.28 37	95.59 26	95.17 21	78.85 82	70.19 224	85.82 241	70.66 37	97.67 51	72.19 182	66.52 287	94.09 122
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
xiu_mvs_v1_base_debu		82.16 123	81.12 126	85.26 113	86.42 233	68.72 70	92.59 135	90.44 205	73.12 168	84.20 70	94.36 84	38.04 325	95.73 153	84.12 90	86.81 124	91.33 197

xiu_mvs_v1_base		82.16 123	81.12 126	85.26 113	86.42 233	68.72 70	92.59 135	90.44 205	73.12 168	84.20 70	94.36 84	38.04 325	95.73 153	84.12 90	86.81 124	91.33 197

xiu_mvs_v1_base_debi		82.16 123	81.12 126	85.26 113	86.42 233	68.72 70	92.59 135	90.44 205	73.12 168	84.20 70	94.36 84	38.04 325	95.73 153	84.12 90	86.81 124	91.33 197

3Dnovator+	73.60 7	82.10 126	80.60 138	86.60 66	90.89 130	66.80 121	95.20 34	93.44 85	74.05 147	67.42 263	92.49 130	49.46 255	97.65 55	70.80 193	91.68 75	95.33 63

MVS_111021_LR		82.02 127	81.52 122	83.51 172	88.42 186	62.88 227	89.77 242	88.93 272	76.78 113	75.55 162	93.10 113	50.31 247	95.38 173	83.82 94	87.02 122	92.26 183

PMMVS		81.98 128	82.04 115	81.78 215	89.76 152	56.17 324	91.13 202	90.69 195	77.96 93	80.09 110	93.57 108	46.33 284	94.99 184	81.41 109	87.46 118	94.17 117

baseline1		81.84 129	81.03 130	84.28 152	91.60 112	66.62 126	91.08 203	91.66 160	81.87 31	74.86 168	91.67 150	69.98 41	94.92 188	71.76 186	64.75 301	91.29 202

EPP-MVSNet		81.79 130	81.52 122	82.61 191	88.77 179	60.21 280	93.02 115	93.66 75	68.52 265	72.90 188	90.39 171	72.19 31	94.96 185	74.93 158	79.29 194	92.67 166

test_vis1_n_1920		81.66 131	82.01 116	80.64 242	82.24 297	55.09 332	94.76 47	86.87 313	81.67 34	84.40 69	94.63 77	38.17 322	94.67 197	91.98 26	83.34 155	92.16 186

APD-MVS_3200maxsize		81.64 132	81.32 124	82.59 192	92.36 88	58.74 300	91.39 186	91.01 191	63.35 302	79.72 114	94.62 78	51.82 233	96.14 134	79.71 121	87.93 113	92.89 163

ACMMP		81.49 133	80.67 135	83.93 159	91.71 110	62.90 226	92.13 149	92.22 131	71.79 207	71.68 208	93.49 110	50.32 246	96.96 102	78.47 134	84.22 151	91.93 189
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
CDS-MVSNet		81.43 134	80.74 133	83.52 170	86.26 237	64.45 175	92.09 152	90.65 199	75.83 124	73.95 179	89.81 184	63.97 92	92.91 259	71.27 189	82.82 159	93.20 151
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 135	79.99 146	85.46 103	90.39 139	68.40 76	86.88 291	90.61 200	74.41 140	70.31 223	84.67 252	63.79 95	92.32 283	73.13 168	85.70 136	95.67 49

ECVR-MVS		81.29 136	80.38 142	84.01 158	88.39 188	61.96 245	92.56 138	86.79 315	77.66 100	76.63 150	91.42 154	46.34 283	95.24 178	74.36 163	89.23 100	94.85 86

thisisatest0530		81.15 137	80.07 143	84.39 147	88.26 192	65.63 149	91.40 184	94.62 40	71.27 225	70.93 214	89.18 191	72.47 28	96.04 142	65.62 247	76.89 217	91.49 193

Fast-Effi-MVS+		81.14 138	80.01 145	84.51 143	90.24 141	65.86 144	94.12 62	89.15 260	73.81 155	75.37 164	88.26 203	57.26 170	94.53 204	66.97 232	84.92 140	93.15 152

HQP-MVS		81.14 138	80.64 136	82.64 190	87.54 211	63.66 204	94.06 63	91.70 158	79.80 61	74.18 173	90.30 173	51.63 237	95.61 161	77.63 139	78.90 196	88.63 235

hse-mvs2		81.12 140	81.11 129	81.16 229	86.52 232	57.48 313	89.40 250	91.16 179	81.45 36	82.73 82	90.49 169	60.11 140	94.58 198	87.69 55	60.41 341	91.41 196

SR-MVS-dyc-post		81.06 141	80.70 134	82.15 206	92.02 97	58.56 302	90.90 207	90.45 202	62.76 309	78.89 124	94.46 80	51.26 241	95.61 161	78.77 132	86.77 127	92.28 179

HyFIR lowres test		81.03 142	79.56 153	85.43 104	87.81 207	68.11 87	90.18 231	90.01 227	70.65 239	72.95 187	86.06 239	63.61 100	94.50 206	75.01 157	79.75 189	93.67 138

nrg030		80.93 143	79.86 148	84.13 155	83.69 282	68.83 67	93.23 107	91.20 177	75.55 127	75.06 166	88.22 206	63.04 112	94.74 192	81.88 104	66.88 284	88.82 233

Vis-MVSNet		80.92 144	79.98 147	83.74 162	88.48 183	61.80 247	93.44 102	88.26 297	73.96 151	77.73 137	91.76 147	49.94 251	94.76 190	65.84 244	90.37 93	94.65 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 145	80.02 144	83.33 176	87.87 204	60.76 268	92.62 131	86.86 314	77.86 96	75.73 157	91.39 156	46.35 282	94.70 196	72.79 173	88.68 106	94.52 107

UWE-MVS		80.81 146	81.01 131	80.20 252	89.33 162	57.05 318	91.91 163	94.71 35	75.67 125	75.01 167	89.37 189	63.13 110	91.44 305	67.19 229	82.80 161	92.12 187

1314		80.70 147	78.95 164	85.94 87	87.77 209	67.56 100	87.91 275	92.55 121	72.17 194	67.44 262	93.09 114	50.27 248	97.04 93	71.68 188	87.64 116	93.23 150

tpmrst		80.57 148	79.14 163	84.84 124	90.10 145	68.28 80	81.70 326	89.72 239	77.63 102	75.96 155	79.54 321	64.94 80	92.71 266	75.43 152	77.28 214	93.55 141

1112_ss		80.56 149	79.83 149	82.77 186	88.65 180	60.78 266	92.29 143	88.36 291	72.58 180	72.46 197	94.95 66	65.09 77	93.42 246	66.38 238	77.71 205	94.10 121

VDDNet		80.50 150	78.26 172	87.21 47	86.19 238	69.79 47	94.48 51	91.31 172	60.42 328	79.34 119	90.91 162	38.48 320	96.56 120	82.16 102	81.05 178	95.27 70

BH-w/o		80.49 151	79.30 160	84.05 157	90.83 132	64.36 183	93.60 92	89.42 247	74.35 142	69.09 235	90.15 178	55.23 199	95.61 161	64.61 255	86.43 133	92.17 185

test_cas_vis1_n_1920		80.45 152	80.61 137	79.97 261	78.25 345	57.01 320	94.04 67	88.33 292	79.06 80	82.81 81	93.70 104	38.65 317	91.63 297	90.82 35	79.81 187	91.27 203

TAMVS		80.37 153	79.45 156	83.13 181	85.14 258	63.37 212	91.23 197	90.76 194	74.81 138	72.65 191	88.49 197	60.63 135	92.95 254	69.41 205	81.95 171	93.08 155

HQP_MVS		80.34 154	79.75 150	82.12 208	86.94 226	62.42 234	93.13 109	91.31 172	78.81 84	72.53 194	89.14 193	50.66 244	95.55 167	76.74 142	78.53 201	88.39 241

SDMVSNet		80.26 155	78.88 165	84.40 146	89.25 165	67.63 99	85.35 297	93.02 100	76.77 114	70.84 215	87.12 225	47.95 271	96.09 137	85.04 80	74.55 227	89.48 227

HPM-MVS_fast		80.25 156	79.55 155	82.33 198	91.55 115	59.95 283	91.32 193	89.16 258	65.23 290	74.71 170	93.07 116	47.81 273	95.74 152	74.87 161	88.23 109	91.31 201

ab-mvs		80.18 157	78.31 171	85.80 93	88.44 185	65.49 155	83.00 319	92.67 114	71.82 206	77.36 143	85.01 248	54.50 206	96.59 117	76.35 146	75.63 224	95.32 65

IS-MVSNet		80.14 158	79.41 157	82.33 198	87.91 202	60.08 282	91.97 161	88.27 295	72.90 175	71.44 211	91.73 149	61.44 127	93.66 241	62.47 272	86.53 131	93.24 149

test-LLR		80.10 159	79.56 153	81.72 217	86.93 228	61.17 258	92.70 126	91.54 163	71.51 221	75.62 159	86.94 227	53.83 215	92.38 279	72.21 180	84.76 143	91.60 191

PVSNet	73.49 8	80.05 160	78.63 167	84.31 150	90.92 129	64.97 166	92.47 139	91.05 189	79.18 75	72.43 198	90.51 168	37.05 337	94.06 224	68.06 218	86.00 134	93.90 133

UA-Net		80.02 161	79.65 151	81.11 231	89.33 162	57.72 309	86.33 294	89.00 271	77.44 105	81.01 97	89.15 192	59.33 151	95.90 146	61.01 279	84.28 149	89.73 223

test-mter		79.96 162	79.38 159	81.72 217	86.93 228	61.17 258	92.70 126	91.54 163	73.85 153	75.62 159	86.94 227	49.84 253	92.38 279	72.21 180	84.76 143	91.60 191

QAPM		79.95 163	77.39 189	87.64 33	89.63 154	71.41 20	93.30 105	93.70 73	65.34 289	67.39 265	91.75 148	47.83 272	98.96 16	57.71 295	89.81 96	92.54 170

UGNet		79.87 164	78.68 166	83.45 175	89.96 147	61.51 254	92.13 149	90.79 193	76.83 112	78.85 129	86.33 235	38.16 323	96.17 133	67.93 221	87.17 121	92.67 166
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
tpm2		79.80 165	77.95 178	85.34 109	88.28 191	68.26 81	81.56 328	91.42 169	70.11 244	77.59 141	80.50 307	67.40 58	94.26 215	67.34 226	77.35 212	93.51 142

thres200		79.66 166	78.33 170	83.66 169	92.54 87	65.82 146	93.06 111	96.31 3	74.90 137	73.30 184	88.66 195	59.67 146	95.61 161	47.84 333	78.67 199	89.56 226

CPTT-MVS		79.59 167	79.16 162	80.89 240	91.54 116	59.80 285	92.10 151	88.54 288	60.42 328	72.96 186	93.28 112	48.27 266	92.80 263	78.89 131	86.50 132	90.06 216

Test_1112_low_res		79.56 168	78.60 168	82.43 194	88.24 194	60.39 277	92.09 152	87.99 302	72.10 196	71.84 204	87.42 220	64.62 85	93.04 250	65.80 245	77.30 213	93.85 135

tttt0517		79.50 169	78.53 169	82.41 197	87.22 219	61.43 256	89.75 243	94.76 32	69.29 254	67.91 255	88.06 210	72.92 25	95.63 159	62.91 268	73.90 237	90.16 215

FIs		79.47 170	79.41 157	79.67 268	85.95 243	59.40 290	91.68 176	93.94 63	78.06 92	68.96 240	88.28 201	66.61 64	91.77 294	66.20 241	74.99 226	87.82 246

BH-RMVSNet		79.46 171	77.65 181	84.89 122	91.68 111	65.66 147	93.55 94	88.09 300	72.93 172	73.37 183	91.12 160	46.20 286	96.12 135	56.28 300	85.61 138	92.91 161

PCF-MVS	73.15 9	79.29 172	77.63 182	84.29 151	86.06 241	65.96 142	87.03 287	91.10 183	69.86 248	69.79 231	90.64 164	57.54 169	96.59 117	64.37 257	82.29 163	90.32 213
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 173	79.57 152	78.24 288	88.46 184	52.29 343	90.41 223	89.12 262	74.24 144	69.13 234	91.91 145	65.77 71	90.09 320	59.00 291	88.09 111	92.33 176

114514_t		79.17 174	77.67 180	83.68 167	95.32 29	65.53 153	92.85 120	91.60 162	63.49 300	67.92 254	90.63 166	46.65 279	95.72 157	67.01 231	83.54 153	89.79 221

FA-MVS(test-final)		79.12 175	77.23 191	84.81 128	90.54 135	63.98 192	81.35 331	91.71 155	71.09 230	74.85 169	82.94 270	52.85 226	97.05 90	67.97 219	81.73 174	93.41 144

VPA-MVSNet		79.03 176	78.00 176	82.11 211	85.95 243	64.48 174	93.22 108	94.66 38	75.05 135	74.04 178	84.95 249	52.17 232	93.52 243	74.90 160	67.04 283	88.32 243

OPM-MVS		79.00 177	78.09 174	81.73 216	83.52 285	63.83 194	91.64 178	90.30 212	76.36 120	71.97 203	89.93 183	46.30 285	95.17 180	75.10 155	77.70 206	86.19 276
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 178	78.22 173	81.25 226	85.33 253	62.73 230	89.53 247	93.21 91	72.39 187	72.14 201	90.13 179	60.99 130	94.72 193	67.73 223	72.49 247	86.29 273

AdaColmap		78.94 179	77.00 195	84.76 130	96.34 17	65.86 144	92.66 130	87.97 304	62.18 314	70.56 217	92.37 134	43.53 300	97.35 73	64.50 256	82.86 158	91.05 206

GeoE		78.90 180	77.43 185	83.29 177	88.95 174	62.02 243	92.31 142	86.23 320	70.24 243	71.34 212	89.27 190	54.43 210	94.04 227	63.31 264	80.81 182	93.81 136

miper_enhance_ethall		78.86 181	77.97 177	81.54 221	88.00 201	65.17 160	91.41 182	89.15 260	75.19 133	68.79 243	83.98 261	67.17 59	92.82 261	72.73 174	65.30 292	86.62 270

VPNet		78.82 182	77.53 184	82.70 188	84.52 269	66.44 130	93.93 72	92.23 128	80.46 50	72.60 192	88.38 200	49.18 259	93.13 249	72.47 178	63.97 311	88.55 238

EPNet_dtu		78.80 183	79.26 161	77.43 296	88.06 198	49.71 356	91.96 162	91.95 141	77.67 99	76.56 152	91.28 158	58.51 158	90.20 318	56.37 299	80.95 179	92.39 174
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 184	77.43 185	82.88 184	92.21 93	64.49 172	92.05 155	96.28 4	73.48 162	71.75 206	88.26 203	60.07 142	95.32 174	45.16 344	77.58 208	88.83 231

TR-MVS		78.77 185	77.37 190	82.95 183	90.49 136	60.88 264	93.67 88	90.07 222	70.08 245	74.51 171	91.37 157	45.69 289	95.70 158	60.12 285	80.32 184	92.29 178

thres400		78.68 186	77.43 185	82.43 194	92.21 93	64.49 172	92.05 155	96.28 4	73.48 162	71.75 206	88.26 203	60.07 142	95.32 174	45.16 344	77.58 208	87.48 250

BH-untuned		78.68 186	77.08 192	83.48 174	89.84 149	63.74 197	92.70 126	88.59 286	71.57 218	66.83 272	88.65 196	51.75 235	95.39 172	59.03 290	84.77 142	91.32 200

OMC-MVS		78.67 188	77.91 179	80.95 238	85.76 248	57.40 315	88.49 266	88.67 282	73.85 153	72.43 198	92.10 140	49.29 258	94.55 203	72.73 174	77.89 204	90.91 207

tpm		78.58 189	77.03 193	83.22 179	85.94 245	64.56 170	83.21 316	91.14 182	78.31 89	73.67 181	79.68 319	64.01 91	92.09 288	66.07 242	71.26 257	93.03 157

OpenMVS	70.45 11	78.54 190	75.92 209	86.41 75	85.93 246	71.68 18	92.74 123	92.51 122	66.49 280	64.56 286	91.96 142	43.88 299	98.10 37	54.61 305	90.65 90	89.44 229

EPMVS		78.49 191	75.98 208	86.02 84	91.21 124	69.68 51	80.23 340	91.20 177	75.25 132	72.48 196	78.11 329	54.65 205	93.69 240	57.66 296	83.04 157	94.69 95

AUN-MVS		78.37 192	77.43 185	81.17 228	86.60 231	57.45 314	89.46 249	91.16 179	74.11 146	74.40 172	90.49 169	55.52 196	94.57 200	74.73 162	60.43 340	91.48 194

thres100view900		78.37 192	77.01 194	82.46 193	91.89 105	63.21 216	91.19 201	96.33 1	72.28 190	70.45 220	87.89 212	60.31 137	95.32 174	45.16 344	77.58 208	88.83 231

GA-MVS		78.33 194	76.23 204	84.65 136	83.65 283	66.30 134	91.44 180	90.14 220	76.01 122	70.32 222	84.02 260	42.50 304	94.72 193	70.98 191	77.00 216	92.94 160

cascas		78.18 195	75.77 211	85.41 105	87.14 221	69.11 60	92.96 116	91.15 181	66.71 278	70.47 218	86.07 238	37.49 331	96.48 125	70.15 199	79.80 188	90.65 209

UniMVSNet_NR-MVSNet		78.15 196	77.55 183	79.98 259	84.46 271	60.26 278	92.25 144	93.20 93	77.50 104	68.88 241	86.61 230	66.10 67	92.13 286	66.38 238	62.55 318	87.54 248

thres600view7		78.00 197	76.66 199	82.03 213	91.93 102	63.69 202	91.30 194	96.33 1	72.43 185	70.46 219	87.89 212	60.31 137	94.92 188	42.64 356	76.64 218	87.48 250

FC-MVSNet-test		77.99 198	78.08 175	77.70 291	84.89 264	55.51 329	90.27 228	93.75 72	76.87 109	66.80 273	87.59 217	65.71 72	90.23 317	62.89 269	73.94 235	87.37 253

Anonymous202405211		77.96 199	75.33 218	85.87 89	93.73 53	64.52 171	94.85 44	85.36 329	62.52 312	76.11 154	90.18 176	29.43 365	97.29 77	68.51 216	77.24 215	95.81 48

cl22		77.94 200	76.78 197	81.42 223	87.57 210	64.93 168	90.67 216	88.86 275	72.45 184	67.63 261	82.68 274	64.07 90	92.91 259	71.79 184	65.30 292	86.44 271

XXY-MVS		77.94 200	76.44 201	82.43 194	82.60 294	64.44 176	92.01 157	91.83 150	73.59 161	70.00 227	85.82 241	54.43 210	94.76 190	69.63 202	68.02 277	88.10 245

MS-PatchMatch		77.90 202	76.50 200	82.12 208	85.99 242	69.95 42	91.75 174	92.70 112	73.97 150	62.58 307	84.44 256	41.11 309	95.78 149	63.76 261	92.17 67	80.62 351

FMVSNet3		77.73 203	76.04 207	82.80 185	91.20 125	68.99 64	91.87 165	91.99 139	73.35 164	67.04 268	83.19 269	56.62 183	92.14 285	59.80 287	69.34 264	87.28 257

miper_ehance_all_eth		77.60 204	76.44 201	81.09 235	85.70 250	64.41 179	90.65 217	88.64 284	72.31 188	67.37 266	82.52 275	64.77 84	92.64 272	70.67 195	65.30 292	86.24 275

UniMVSNet (Re)		77.58 205	76.78 197	79.98 259	84.11 277	60.80 265	91.76 172	93.17 95	76.56 118	69.93 230	84.78 251	63.32 107	92.36 281	64.89 254	62.51 320	86.78 265

PatchmatchNet		77.46 206	74.63 225	85.96 86	89.55 157	70.35 36	79.97 345	89.55 242	72.23 191	70.94 213	76.91 340	57.03 173	92.79 264	54.27 307	81.17 177	94.74 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 207	75.65 214	82.73 187	80.38 315	67.13 112	91.85 167	90.23 216	75.09 134	69.37 232	83.39 267	53.79 217	94.44 207	71.77 185	65.00 298	86.63 269

CHOSEN 280x420		77.35 208	76.95 196	78.55 283	87.07 223	62.68 231	69.71 376	82.95 351	68.80 261	71.48 210	87.27 224	66.03 68	84.00 361	76.47 145	82.81 160	88.95 230

PS-MVSNAJss		77.26 209	76.31 203	80.13 254	80.64 313	59.16 295	90.63 220	91.06 188	72.80 176	68.58 247	84.57 254	53.55 219	93.96 232	72.97 169	71.96 251	87.27 258

gg-mvs-nofinetune		77.18 210	74.31 232	85.80 93	91.42 118	68.36 77	71.78 370	94.72 34	49.61 371	77.12 146	45.92 395	77.41 8	93.98 231	67.62 224	93.16 55	95.05 79

WB-MVSnew		77.14 211	76.18 206	80.01 258	86.18 239	63.24 215	91.26 195	94.11 60	71.72 210	73.52 182	87.29 223	45.14 294	93.00 252	56.98 297	79.42 190	83.80 312

MVP-Stereo		77.12 212	76.23 204	79.79 266	81.72 302	66.34 133	89.29 251	90.88 192	70.56 240	62.01 311	82.88 271	49.34 256	94.13 219	65.55 249	93.80 42	78.88 365
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 213	75.37 216	82.20 204	89.25 165	62.11 242	82.06 323	89.09 264	76.77 114	70.84 215	87.12 225	41.43 308	95.01 183	67.23 228	74.55 227	89.48 227

dmvs_re		76.93 214	75.36 217	81.61 219	87.78 208	60.71 271	80.00 344	87.99 302	79.42 69	69.02 238	89.47 188	46.77 277	94.32 209	63.38 263	74.45 230	89.81 220

bld_raw_dy_0_64		76.92 215	74.65 224	83.71 166	84.96 263	71.37 21	73.29 368	89.16 258	50.14 370	62.32 309	84.19 258	67.48 57	95.61 161	72.10 183	88.25 108	84.14 308

X-MVStestdata		76.86 216	74.13 236	85.05 118	93.22 64	63.78 195	92.92 117	92.66 115	73.99 148	78.18 133	10.19 410	55.25 197	97.41 68	79.16 126	91.58 77	93.95 129

DU-MVS		76.86 216	75.84 210	79.91 262	82.96 291	60.26 278	91.26 195	91.54 163	76.46 119	68.88 241	86.35 233	56.16 188	92.13 286	66.38 238	62.55 318	87.35 255

mvsmamba		76.85 218	75.71 213	80.25 250	83.07 290	59.16 295	91.44 180	80.64 358	76.84 111	67.95 253	86.33 235	46.17 287	94.24 216	76.06 147	72.92 243	87.36 254

Anonymous20240529		76.84 219	74.15 235	84.88 123	91.02 126	64.95 167	93.84 80	91.09 184	53.57 358	73.00 185	87.42 220	35.91 341	97.32 75	69.14 210	72.41 249	92.36 175

c3_l		76.83 220	75.47 215	80.93 239	85.02 261	64.18 189	90.39 224	88.11 299	71.66 211	66.65 274	81.64 287	63.58 103	92.56 273	69.31 207	62.86 315	86.04 281

WR-MVS		76.76 221	75.74 212	79.82 265	84.60 267	62.27 240	92.60 133	92.51 122	76.06 121	67.87 258	85.34 245	56.76 179	90.24 316	62.20 273	63.69 313	86.94 263

v1144		76.73 222	74.88 221	82.27 200	80.23 319	66.60 127	91.68 176	90.21 218	73.69 158	69.06 237	81.89 282	52.73 228	94.40 208	69.21 208	65.23 295	85.80 287

IterMVS-LS		76.49 223	75.18 220	80.43 245	84.49 270	62.74 229	90.64 218	88.80 277	72.40 186	65.16 281	81.72 285	60.98 131	92.27 284	67.74 222	64.65 303	86.29 273
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 224	74.55 228	82.19 205	79.14 333	67.82 93	90.26 229	89.42 247	73.75 156	68.63 246	81.89 282	51.31 240	94.09 221	71.69 187	64.84 299	84.66 304

v148		76.19 225	74.47 230	81.36 224	80.05 321	64.44 176	91.75 174	90.23 216	73.68 159	67.13 267	80.84 302	55.92 193	93.86 238	68.95 212	61.73 329	85.76 290

Effi-MVS+-dtu		76.14 226	75.28 219	78.72 282	83.22 287	55.17 331	89.87 240	87.78 305	75.42 129	67.98 252	81.43 291	45.08 295	92.52 275	75.08 156	71.63 252	88.48 239

cl____		76.07 227	74.67 222	80.28 248	85.15 257	61.76 249	90.12 232	88.73 279	71.16 226	65.43 278	81.57 289	61.15 128	92.95 254	66.54 235	62.17 322	86.13 279

DIV-MVS_self_test		76.07 227	74.67 222	80.28 248	85.14 258	61.75 250	90.12 232	88.73 279	71.16 226	65.42 279	81.60 288	61.15 128	92.94 258	66.54 235	62.16 324	86.14 277

FMVSNet2		76.07 227	74.01 238	82.26 202	88.85 175	67.66 97	91.33 192	91.61 161	70.84 234	65.98 275	82.25 278	48.03 267	92.00 290	58.46 292	68.73 272	87.10 260

v144192		76.05 230	74.03 237	82.12 208	79.50 327	66.55 129	91.39 186	89.71 240	72.30 189	68.17 250	81.33 294	51.75 235	94.03 229	67.94 220	64.19 306	85.77 288

NR-MVSNet		76.05 230	74.59 226	80.44 244	82.96 291	62.18 241	90.83 211	91.73 153	77.12 108	60.96 314	86.35 233	59.28 152	91.80 293	60.74 280	61.34 333	87.35 255

v1192		75.98 232	73.92 239	82.15 206	79.73 323	66.24 136	91.22 198	89.75 234	72.67 178	68.49 248	81.42 292	49.86 252	94.27 213	67.08 230	65.02 297	85.95 284

FE-MVS		75.97 233	73.02 249	84.82 125	89.78 150	65.56 151	77.44 356	91.07 187	64.55 292	72.66 190	79.85 317	46.05 288	96.69 115	54.97 304	80.82 181	92.21 184

eth_miper_zixun_eth		75.96 234	74.40 231	80.66 241	84.66 266	63.02 220	89.28 252	88.27 295	71.88 202	65.73 276	81.65 286	59.45 148	92.81 262	68.13 217	60.53 338	86.14 277

TranMVSNet+NR-MVSNet		75.86 235	74.52 229	79.89 263	82.44 296	60.64 274	91.37 189	91.37 170	76.63 116	67.65 260	86.21 237	52.37 231	91.55 299	61.84 275	60.81 336	87.48 250

SCA		75.82 236	72.76 252	85.01 120	86.63 230	70.08 38	81.06 333	89.19 256	71.60 217	70.01 226	77.09 338	45.53 290	90.25 313	60.43 282	73.27 239	94.68 96

LPG-MVS_test		75.82 236	74.58 227	79.56 272	84.31 274	59.37 291	90.44 221	89.73 237	69.49 251	64.86 282	88.42 198	38.65 317	94.30 211	72.56 176	72.76 244	85.01 301

GBi-Net		75.65 238	73.83 240	81.10 232	88.85 175	65.11 162	90.01 236	90.32 208	70.84 234	67.04 268	80.25 312	48.03 267	91.54 300	59.80 287	69.34 264	86.64 266

test1		75.65 238	73.83 240	81.10 232	88.85 175	65.11 162	90.01 236	90.32 208	70.84 234	67.04 268	80.25 312	48.03 267	91.54 300	59.80 287	69.34 264	86.64 266

v1921920		75.63 240	73.49 245	82.06 212	79.38 328	66.35 132	91.07 205	89.48 243	71.98 197	67.99 251	81.22 297	49.16 261	93.90 235	66.56 234	64.56 304	85.92 286

ACMP	71.68 10	75.58 241	74.23 234	79.62 270	84.97 262	59.64 286	90.80 212	89.07 266	70.39 241	62.95 303	87.30 222	38.28 321	93.87 236	72.89 170	71.45 255	85.36 297
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 242	73.26 247	81.61 219	80.67 312	66.82 119	89.54 246	89.27 252	71.65 212	63.30 299	80.30 311	54.99 203	94.06 224	67.33 227	62.33 321	83.94 310

tpm cat1		75.30 243	72.21 261	84.58 140	88.52 181	67.77 94	78.16 354	88.02 301	61.88 320	68.45 249	76.37 344	60.65 134	94.03 229	53.77 310	74.11 233	91.93 189

PLC	68.80 14	75.23 244	73.68 243	79.86 264	92.93 74	58.68 301	90.64 218	88.30 293	60.90 325	64.43 290	90.53 167	42.38 305	94.57 200	56.52 298	76.54 219	86.33 272
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 245	72.98 250	81.88 214	79.20 330	66.00 140	90.75 214	89.11 263	71.63 216	67.41 264	81.22 297	47.36 275	93.87 236	65.46 250	64.72 302	85.77 288

Fast-Effi-MVS+-dtu		75.04 246	73.37 246	80.07 255	80.86 308	59.52 289	91.20 200	85.38 328	71.90 200	65.20 280	84.84 250	41.46 307	92.97 253	66.50 237	72.96 242	87.73 247

dp		75.01 247	72.09 262	83.76 161	89.28 164	66.22 137	79.96 346	89.75 234	71.16 226	67.80 259	77.19 337	51.81 234	92.54 274	50.39 318	71.44 256	92.51 173

TAPA-MVS	70.22 12	74.94 248	73.53 244	79.17 277	90.40 138	52.07 344	89.19 255	89.61 241	62.69 311	70.07 225	92.67 126	48.89 264	94.32 209	38.26 370	79.97 186	91.12 205
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 249	72.54 258	81.46 222	80.33 317	66.71 123	89.15 256	89.08 265	70.94 232	63.08 302	79.86 316	52.52 229	94.04 227	65.70 246	62.17 322	83.64 313

XVG-OURS-SEG-HR		74.70 250	73.08 248	79.57 271	78.25 345	57.33 316	80.49 336	87.32 308	63.22 304	68.76 244	90.12 182	44.89 296	91.59 298	70.55 197	74.09 234	89.79 221

ACMM	69.62 13	74.34 251	72.73 254	79.17 277	84.25 276	57.87 307	90.36 225	89.93 228	63.17 306	65.64 277	86.04 240	37.79 329	94.10 220	65.89 243	71.52 254	85.55 293
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 252	72.30 260	80.32 246	91.49 117	61.66 252	90.85 210	80.72 357	56.67 350	63.85 294	90.64 164	46.75 278	90.84 308	53.79 309	75.99 223	88.47 240

XVG-OURS		74.25 253	72.46 259	79.63 269	78.45 343	57.59 312	80.33 338	87.39 307	63.86 297	68.76 244	89.62 186	40.50 311	91.72 295	69.00 211	74.25 232	89.58 224

test_fmvs1		74.07 254	73.69 242	75.22 314	78.91 337	47.34 369	89.06 259	74.69 372	63.68 299	79.41 118	91.59 152	24.36 374	87.77 338	85.22 77	76.26 221	90.55 212

CVMVSNet		74.04 255	74.27 233	73.33 329	85.33 253	43.94 381	89.53 247	88.39 290	54.33 357	70.37 221	90.13 179	49.17 260	84.05 359	61.83 276	79.36 192	91.99 188

Baseline_NR-MVSNet		73.99 256	72.83 251	77.48 295	80.78 310	59.29 294	91.79 169	84.55 337	68.85 260	68.99 239	80.70 303	56.16 188	92.04 289	62.67 270	60.98 335	81.11 345

pmmvs4		73.92 257	71.81 266	80.25 250	79.17 331	65.24 158	87.43 283	87.26 310	67.64 272	63.46 297	83.91 262	48.96 263	91.53 303	62.94 267	65.49 291	83.96 309

D2MVS		73.80 258	72.02 263	79.15 279	79.15 332	62.97 221	88.58 265	90.07 222	72.94 171	59.22 323	78.30 326	42.31 306	92.70 268	65.59 248	72.00 250	81.79 340

CR-MVSNet		73.79 259	70.82 274	82.70 188	83.15 288	67.96 90	70.25 373	84.00 342	73.67 160	69.97 228	72.41 358	57.82 166	89.48 324	52.99 313	73.13 240	90.64 210

test_djsdf		73.76 260	72.56 257	77.39 297	77.00 355	53.93 337	89.07 257	90.69 195	65.80 284	63.92 292	82.03 281	43.14 303	92.67 269	72.83 171	68.53 273	85.57 292

pmmvs5		73.35 261	71.52 268	78.86 281	78.64 341	60.61 275	91.08 203	86.90 312	67.69 269	63.32 298	83.64 263	44.33 298	90.53 310	62.04 274	66.02 289	85.46 295

Anonymous20231211		73.08 262	70.39 278	81.13 230	90.62 134	63.33 213	91.40 184	90.06 224	51.84 363	64.46 289	80.67 305	36.49 339	94.07 223	63.83 260	64.17 307	85.98 283

tt0805		73.07 263	70.73 275	80.07 255	78.37 344	57.05 318	87.78 278	92.18 134	61.23 324	67.04 268	86.49 232	31.35 359	94.58 198	65.06 253	67.12 282	88.57 237

miper_lstm_enhance		73.05 264	71.73 267	77.03 301	83.80 280	58.32 304	81.76 324	88.88 273	69.80 249	61.01 313	78.23 328	57.19 171	87.51 342	65.34 251	59.53 343	85.27 300

jajsoiax		73.05 264	71.51 269	77.67 292	77.46 352	54.83 333	88.81 261	90.04 225	69.13 258	62.85 305	83.51 265	31.16 360	92.75 265	70.83 192	69.80 260	85.43 296

LCM-MVSNet-Re		72.93 266	71.84 265	76.18 310	88.49 182	48.02 364	80.07 343	70.17 383	73.96 151	52.25 354	80.09 315	49.98 250	88.24 332	67.35 225	84.23 150	92.28 179

pm-mvs1		72.89 267	71.09 271	78.26 287	79.10 334	57.62 311	90.80 212	89.30 251	67.66 270	62.91 304	81.78 284	49.11 262	92.95 254	60.29 284	58.89 346	84.22 307

tpmvs		72.88 268	69.76 284	82.22 203	90.98 127	67.05 114	78.22 353	88.30 293	63.10 307	64.35 291	74.98 351	55.09 202	94.27 213	43.25 350	69.57 263	85.34 298

test0.0.03 1		72.76 269	72.71 255	72.88 333	80.25 318	47.99 365	91.22 198	89.45 245	71.51 221	62.51 308	87.66 215	53.83 215	85.06 355	50.16 320	67.84 280	85.58 291

UniMVSNet_ETH3D		72.74 270	70.53 277	79.36 274	78.62 342	56.64 322	85.01 299	89.20 255	63.77 298	64.84 284	84.44 256	34.05 348	91.86 292	63.94 259	70.89 259	89.57 225

mvs_tets		72.71 271	71.11 270	77.52 293	77.41 353	54.52 335	88.45 267	89.76 233	68.76 263	62.70 306	83.26 268	29.49 364	92.71 266	70.51 198	69.62 262	85.34 298

FMVSNet1		72.71 271	69.91 282	81.10 232	83.60 284	65.11 162	90.01 236	90.32 208	63.92 296	63.56 296	80.25 312	36.35 340	91.54 300	54.46 306	66.75 285	86.64 266

test_fmvs1_n		72.69 273	71.92 264	74.99 317	71.15 375	47.08 371	87.34 285	75.67 367	63.48 301	78.08 135	91.17 159	20.16 385	87.87 335	84.65 86	75.57 225	90.01 218

IterMVS		72.65 274	70.83 272	78.09 289	82.17 298	62.96 222	87.64 281	86.28 318	71.56 219	60.44 316	78.85 324	45.42 292	86.66 346	63.30 265	61.83 326	84.65 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 275	72.74 253	72.10 341	87.87 204	49.45 358	88.07 271	89.01 268	72.91 173	63.11 300	88.10 207	63.63 98	85.54 351	32.73 384	69.23 267	81.32 343

PatchMatch-RL		72.06 276	69.98 279	78.28 286	89.51 158	55.70 328	83.49 308	83.39 349	61.24 323	63.72 295	82.76 272	34.77 345	93.03 251	53.37 312	77.59 207	86.12 280

PVSNet_0	68.08 15	71.81 277	68.32 293	82.27 200	84.68 265	62.31 239	88.68 263	90.31 211	75.84 123	57.93 334	80.65 306	37.85 328	94.19 217	69.94 200	29.05 399	90.31 214

MIMVSNet		71.64 278	68.44 291	81.23 227	81.97 301	64.44 176	73.05 369	88.80 277	69.67 250	64.59 285	74.79 352	32.79 351	87.82 336	53.99 308	76.35 220	91.42 195

test_vis1_n		71.63 279	70.73 275	74.31 324	69.63 381	47.29 370	86.91 289	72.11 377	63.21 305	75.18 165	90.17 177	20.40 383	85.76 350	84.59 87	74.42 231	89.87 219

IterMVS-SCA-FT		71.55 280	69.97 280	76.32 308	81.48 304	60.67 273	87.64 281	85.99 323	66.17 282	59.50 321	78.88 323	45.53 290	83.65 363	62.58 271	61.93 325	84.63 306

v7n		71.31 281	68.65 288	79.28 275	76.40 357	60.77 267	86.71 292	89.45 245	64.17 295	58.77 328	78.24 327	44.59 297	93.54 242	57.76 294	61.75 328	83.52 316

anonymousdsp		71.14 282	69.37 286	76.45 307	72.95 370	54.71 334	84.19 303	88.88 273	61.92 319	62.15 310	79.77 318	38.14 324	91.44 305	68.90 213	67.45 281	83.21 322

F-COLMAP		70.66 283	68.44 291	77.32 298	86.37 236	55.91 326	88.00 273	86.32 317	56.94 348	57.28 338	88.07 209	33.58 349	92.49 276	51.02 316	68.37 274	83.55 314

WR-MVS_H		70.59 284	69.94 281	72.53 335	81.03 307	51.43 347	87.35 284	92.03 138	67.38 273	60.23 318	80.70 303	55.84 194	83.45 365	46.33 340	58.58 348	82.72 329

CP-MVSNet		70.50 285	69.91 282	72.26 338	80.71 311	51.00 350	87.23 286	90.30 212	67.84 268	59.64 320	82.69 273	50.23 249	82.30 373	51.28 315	59.28 344	83.46 318

RPMNet		70.42 286	65.68 306	84.63 138	83.15 288	67.96 90	70.25 373	90.45 202	46.83 380	69.97 228	65.10 378	56.48 187	95.30 177	35.79 375	73.13 240	90.64 210

testing3		70.38 287	70.83 272	69.03 352	85.82 247	43.93 382	90.72 215	90.56 201	68.06 267	60.24 317	86.82 229	64.83 82	84.12 357	26.33 392	64.10 308	79.04 364

tfpnnormal		70.10 288	67.36 297	78.32 285	83.45 286	60.97 263	88.85 260	92.77 109	64.85 291	60.83 315	78.53 325	43.52 301	93.48 244	31.73 387	61.70 330	80.52 352

TransMVSNet (Re)		70.07 289	67.66 295	77.31 299	80.62 314	59.13 297	91.78 171	84.94 333	65.97 283	60.08 319	80.44 308	50.78 243	91.87 291	48.84 326	45.46 376	80.94 347

CL-MVSNet_self_test		69.92 290	68.09 294	75.41 313	73.25 369	55.90 327	90.05 235	89.90 229	69.96 246	61.96 312	76.54 341	51.05 242	87.64 339	49.51 324	50.59 368	82.70 331

DP-MVS		69.90 291	66.48 299	80.14 253	95.36 28	62.93 223	89.56 244	76.11 365	50.27 369	57.69 336	85.23 246	39.68 313	95.73 153	33.35 380	71.05 258	81.78 341

PS-CasMVS		69.86 292	69.13 287	72.07 342	80.35 316	50.57 352	87.02 288	89.75 234	67.27 274	59.19 324	82.28 277	46.58 280	82.24 374	50.69 317	59.02 345	83.39 320

Syy-MVS		69.65 293	69.52 285	70.03 348	87.87 204	43.21 383	88.07 271	89.01 268	72.91 173	63.11 300	88.10 207	45.28 293	85.54 351	22.07 396	69.23 267	81.32 343

MSDG		69.54 294	65.73 305	80.96 237	85.11 260	63.71 200	84.19 303	83.28 350	56.95 347	54.50 345	84.03 259	31.50 357	96.03 143	42.87 354	69.13 269	83.14 324

PEN-MVS		69.46 295	68.56 289	72.17 340	79.27 329	49.71 356	86.90 290	89.24 253	67.24 277	59.08 325	82.51 276	47.23 276	83.54 364	48.42 328	57.12 349	83.25 321

LS3D		69.17 296	66.40 301	77.50 294	91.92 103	56.12 325	85.12 298	80.37 359	46.96 378	56.50 340	87.51 219	37.25 332	93.71 239	32.52 386	79.40 191	82.68 332

PatchT		69.11 297	65.37 310	80.32 246	82.07 300	63.68 203	67.96 383	87.62 306	50.86 367	69.37 232	65.18 377	57.09 172	88.53 330	41.59 359	66.60 286	88.74 234

KD-MVS_2432*1600		69.03 298	66.37 302	77.01 302	85.56 251	61.06 261	81.44 329	90.25 214	67.27 274	58.00 332	76.53 342	54.49 207	87.63 340	48.04 330	35.77 391	82.34 335

miper_refine_blended		69.03 298	66.37 302	77.01 302	85.56 251	61.06 261	81.44 329	90.25 214	67.27 274	58.00 332	76.53 342	54.49 207	87.63 340	48.04 330	35.77 391	82.34 335

mvsany_test1		68.77 300	68.56 289	69.39 350	73.57 368	45.88 377	80.93 334	60.88 396	59.65 334	71.56 209	90.26 175	43.22 302	75.05 384	74.26 164	62.70 317	87.25 259

ACMH	63.93 17	68.62 301	64.81 312	80.03 257	85.22 256	63.25 214	87.72 279	84.66 335	60.83 326	51.57 357	79.43 322	27.29 370	94.96 185	41.76 357	64.84 299	81.88 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 302	65.41 309	77.96 290	78.69 340	62.93 223	89.86 241	89.17 257	60.55 327	50.27 362	77.73 332	22.60 379	94.06 224	47.18 336	72.65 246	76.88 373

ADS-MVSNet		68.54 303	64.38 319	81.03 236	88.06 198	66.90 118	68.01 381	84.02 341	57.57 342	64.48 287	69.87 368	38.68 315	89.21 326	40.87 361	67.89 278	86.97 261

DTE-MVSNet		68.46 304	67.33 298	71.87 344	77.94 349	49.00 362	86.16 295	88.58 287	66.36 281	58.19 329	82.21 279	46.36 281	83.87 362	44.97 347	55.17 356	82.73 328

our_test_3		68.29 305	64.69 314	79.11 280	78.92 335	64.85 169	88.40 268	85.06 331	60.32 330	52.68 352	76.12 346	40.81 310	89.80 323	44.25 349	55.65 354	82.67 333

Patchmatch-RL test		68.17 306	64.49 317	79.19 276	71.22 374	53.93 337	70.07 375	71.54 381	69.22 255	56.79 339	62.89 382	56.58 184	88.61 327	69.53 204	52.61 363	95.03 81

XVG-ACMP-BASELINE		68.04 307	65.53 308	75.56 312	74.06 367	52.37 342	78.43 350	85.88 324	62.03 317	58.91 327	81.21 299	20.38 384	91.15 307	60.69 281	68.18 275	83.16 323

FMVSNet5		68.04 307	65.66 307	75.18 316	84.43 272	57.89 306	83.54 307	86.26 319	61.83 321	53.64 350	73.30 355	37.15 335	85.08 354	48.99 325	61.77 327	82.56 334

ppachtmachnet_test		67.72 309	63.70 321	79.77 267	78.92 335	66.04 139	88.68 263	82.90 352	60.11 332	55.45 342	75.96 347	39.19 314	90.55 309	39.53 365	52.55 364	82.71 330

ACMH+	65.35 16	67.65 310	64.55 315	76.96 304	84.59 268	57.10 317	88.08 270	80.79 356	58.59 340	53.00 351	81.09 301	26.63 372	92.95 254	46.51 338	61.69 331	80.82 348

pmmvs6		67.57 311	64.76 313	76.00 311	72.82 372	53.37 339	88.71 262	86.78 316	53.19 359	57.58 337	78.03 330	35.33 344	92.41 278	55.56 302	54.88 358	82.21 337

Anonymous20231206		67.53 312	65.78 304	72.79 334	74.95 363	47.59 367	88.23 269	87.32 308	61.75 322	58.07 331	77.29 335	37.79 329	87.29 344	42.91 352	63.71 312	83.48 317

Patchmtry		67.53 312	63.93 320	78.34 284	82.12 299	64.38 180	68.72 378	84.00 342	48.23 377	59.24 322	72.41 358	57.82 166	89.27 325	46.10 341	56.68 353	81.36 342

USDC		67.43 314	64.51 316	76.19 309	77.94 349	55.29 330	78.38 351	85.00 332	73.17 166	48.36 370	80.37 309	21.23 381	92.48 277	52.15 314	64.02 310	80.81 349

ADS-MVSNet2		66.90 315	63.44 323	77.26 300	88.06 198	60.70 272	68.01 381	75.56 369	57.57 342	64.48 287	69.87 368	38.68 315	84.10 358	40.87 361	67.89 278	86.97 261

CMPMVS	48.56 21	66.77 316	64.41 318	73.84 326	70.65 378	50.31 353	77.79 355	85.73 327	45.54 382	44.76 380	82.14 280	35.40 343	90.14 319	63.18 266	74.54 229	81.07 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 317	62.92 326	76.80 306	76.51 356	57.77 308	89.22 253	83.41 348	55.48 354	53.86 349	77.84 331	26.28 373	93.95 233	34.90 377	68.76 271	78.68 367

LTVRE_ROB	59.60 19	66.27 318	63.54 322	74.45 321	84.00 279	51.55 346	67.08 384	83.53 346	58.78 338	54.94 344	80.31 310	34.54 346	93.23 248	40.64 363	68.03 276	78.58 368
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
JIA-IIPM		66.06 319	62.45 329	76.88 305	81.42 306	54.45 336	57.49 396	88.67 282	49.36 372	63.86 293	46.86 394	56.06 191	90.25 313	49.53 323	68.83 270	85.95 284

Patchmatch-test		65.86 320	60.94 335	80.62 243	83.75 281	58.83 299	58.91 395	75.26 371	44.50 385	50.95 361	77.09 338	58.81 157	87.90 334	35.13 376	64.03 309	95.12 77

UnsupCasMVSNet_eth		65.79 321	63.10 324	73.88 325	70.71 377	50.29 354	81.09 332	89.88 230	72.58 180	49.25 367	74.77 353	32.57 353	87.43 343	55.96 301	41.04 383	83.90 311

test_fmvs2		65.78 322	64.84 311	68.60 354	66.54 386	41.71 385	83.27 313	69.81 384	54.38 356	67.91 255	84.54 255	15.35 390	81.22 378	75.65 151	66.16 288	82.88 325

dmvs_testset		65.55 323	66.45 300	62.86 364	79.87 322	22.35 410	76.55 358	71.74 379	77.42 107	55.85 341	87.77 214	51.39 239	80.69 379	31.51 390	65.92 290	85.55 293

pmmvs-eth3d		65.53 324	62.32 330	75.19 315	69.39 382	59.59 287	82.80 320	83.43 347	62.52 312	51.30 359	72.49 356	32.86 350	87.16 345	55.32 303	50.73 367	78.83 366

mamv4		65.18 325	67.43 296	58.44 368	77.88 351	49.36 361	69.40 377	70.99 382	48.31 376	57.78 335	85.53 244	59.01 155	51.88 406	73.67 166	64.32 305	74.07 378

SixPastTwentyTwo		64.92 326	61.78 333	74.34 323	78.74 339	49.76 355	83.42 311	79.51 362	62.86 308	50.27 362	77.35 333	30.92 362	90.49 311	45.89 342	47.06 373	82.78 326

OurMVSNet-221017-0		64.68 327	62.17 331	72.21 339	76.08 360	47.35 368	80.67 335	81.02 355	56.19 351	51.60 356	79.66 320	27.05 371	88.56 329	53.60 311	53.63 361	80.71 350

test_0402		64.54 328	61.09 334	74.92 318	84.10 278	60.75 269	87.95 274	79.71 361	52.03 361	52.41 353	77.20 336	32.21 355	91.64 296	23.14 394	61.03 334	72.36 382

testgi		64.48 329	62.87 327	69.31 351	71.24 373	40.62 388	85.49 296	79.92 360	65.36 288	54.18 347	83.49 266	23.74 377	84.55 356	41.60 358	60.79 337	82.77 327

RPSCF		64.24 330	61.98 332	71.01 346	76.10 359	45.00 378	75.83 363	75.94 366	46.94 379	58.96 326	84.59 253	31.40 358	82.00 375	47.76 334	60.33 342	86.04 281

EU-MVSNet		64.01 331	63.01 325	67.02 360	74.40 366	38.86 393	83.27 313	86.19 321	45.11 383	54.27 346	81.15 300	36.91 338	80.01 381	48.79 327	57.02 350	82.19 338

test20.03		63.83 332	62.65 328	67.38 359	70.58 379	39.94 389	86.57 293	84.17 339	63.29 303	51.86 355	77.30 334	37.09 336	82.47 371	38.87 369	54.13 360	79.73 358

MDA-MVSNet_test_wron		63.78 333	60.16 337	74.64 319	78.15 347	60.41 276	83.49 308	84.03 340	56.17 353	39.17 390	71.59 364	37.22 333	83.24 368	42.87 354	48.73 370	80.26 355

YYNet1		63.76 334	60.14 338	74.62 320	78.06 348	60.19 281	83.46 310	83.99 344	56.18 352	39.25 389	71.56 365	37.18 334	83.34 366	42.90 353	48.70 371	80.32 354

K. test v3		63.09 335	59.61 340	73.53 328	76.26 358	49.38 360	83.27 313	77.15 364	64.35 294	47.77 372	72.32 360	28.73 366	87.79 337	49.93 322	36.69 389	83.41 319

COLMAP_ROB	57.96 20	62.98 336	59.65 339	72.98 332	81.44 305	53.00 341	83.75 306	75.53 370	48.34 375	48.81 369	81.40 293	24.14 375	90.30 312	32.95 382	60.52 339	75.65 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 337	59.08 341	71.10 345	67.19 385	48.72 363	83.91 305	85.23 330	50.38 368	47.84 371	71.22 367	20.74 382	85.51 353	46.47 339	58.75 347	79.06 363

AllTest		61.66 338	58.06 343	72.46 336	79.57 324	51.42 348	80.17 341	68.61 386	51.25 365	45.88 374	81.23 295	19.86 386	86.58 347	38.98 367	57.01 351	79.39 360

UnsupCasMVSNet_bld		61.60 339	57.71 344	73.29 330	68.73 383	51.64 345	78.61 349	89.05 267	57.20 346	46.11 373	61.96 385	28.70 367	88.60 328	50.08 321	38.90 387	79.63 359

MDA-MVSNet-bldmvs		61.54 340	57.70 345	73.05 331	79.53 326	57.00 321	83.08 317	81.23 354	57.57 342	34.91 393	72.45 357	32.79 351	86.26 349	35.81 374	41.95 381	75.89 375

KD-MVS_self_test		60.87 341	58.60 342	67.68 357	66.13 387	39.93 390	75.63 365	84.70 334	57.32 345	49.57 365	68.45 372	29.55 363	82.87 369	48.09 329	47.94 372	80.25 356

kuosan		60.86 342	60.24 336	62.71 365	81.57 303	46.43 374	75.70 364	85.88 324	57.98 341	48.95 368	69.53 370	58.42 159	76.53 383	28.25 391	35.87 390	65.15 389

TinyColmap		60.32 343	56.42 350	72.00 343	78.78 338	53.18 340	78.36 352	75.64 368	52.30 360	41.59 388	75.82 349	14.76 393	88.35 331	35.84 373	54.71 359	74.46 377

MVS-HIRNet		60.25 344	55.55 351	74.35 322	84.37 273	56.57 323	71.64 371	74.11 373	34.44 393	45.54 378	42.24 400	31.11 361	89.81 321	40.36 364	76.10 222	76.67 374

MIMVSNet1		60.16 345	57.33 346	68.67 353	69.71 380	44.13 380	78.92 348	84.21 338	55.05 355	44.63 381	71.85 362	23.91 376	81.54 377	32.63 385	55.03 357	80.35 353

PM-MVS		59.40 346	56.59 348	67.84 355	63.63 389	41.86 384	76.76 357	63.22 393	59.01 337	51.07 360	72.27 361	11.72 396	83.25 367	61.34 277	50.28 369	78.39 369

new-patchmatchnet		59.30 347	56.48 349	67.79 356	65.86 388	44.19 379	82.47 321	81.77 353	59.94 333	43.65 384	66.20 376	27.67 369	81.68 376	39.34 366	41.40 382	77.50 372

test_vis1_rt		59.09 348	57.31 347	64.43 362	68.44 384	46.02 376	83.05 318	48.63 405	51.96 362	49.57 365	63.86 381	16.30 388	80.20 380	71.21 190	62.79 316	67.07 388

test_fmvs3		56.82 349	54.86 353	62.69 366	53.59 399	35.47 396	75.87 362	65.64 391	43.91 386	55.10 343	71.43 366	6.91 404	74.40 387	68.64 215	52.63 362	78.20 370

DSMNet-mixed		56.78 350	54.44 354	63.79 363	63.21 390	29.44 405	64.43 387	64.10 392	42.12 390	51.32 358	71.60 363	31.76 356	75.04 385	36.23 372	65.20 296	86.87 264

pmmvs3		55.51 351	51.50 357	67.53 358	57.90 397	50.93 351	80.37 337	73.66 374	40.63 391	44.15 383	64.75 379	16.30 388	78.97 382	44.77 348	40.98 385	72.69 380

TDRefinement		55.28 352	51.58 356	66.39 361	59.53 396	46.15 375	76.23 360	72.80 375	44.60 384	42.49 386	76.28 345	15.29 391	82.39 372	33.20 381	43.75 378	70.62 384

dongtai		55.18 353	55.46 352	54.34 376	76.03 361	36.88 394	76.07 361	84.61 336	51.28 364	43.41 385	64.61 380	56.56 185	67.81 394	18.09 399	28.50 400	58.32 392

LF4IMVS		54.01 354	52.12 355	59.69 367	62.41 392	39.91 391	68.59 379	68.28 388	42.96 389	44.55 382	75.18 350	14.09 395	68.39 393	41.36 360	51.68 365	70.78 383

N_pmnet		50.55 355	49.11 358	54.88 374	77.17 354	4.02 418	84.36 302	2.00 416	48.59 373	45.86 376	68.82 371	32.22 354	82.80 370	31.58 388	51.38 366	77.81 371

new_pmnet		49.31 356	46.44 359	57.93 369	62.84 391	40.74 387	68.47 380	62.96 394	36.48 392	35.09 392	57.81 389	14.97 392	72.18 389	32.86 383	46.44 374	60.88 391

mvsany_test3		48.86 357	46.35 360	56.41 370	46.00 405	31.67 401	62.26 389	47.25 406	43.71 387	45.54 378	68.15 373	10.84 397	64.44 402	57.95 293	35.44 393	73.13 379

test_f		46.58 358	43.45 362	55.96 371	45.18 406	32.05 400	61.18 390	49.49 404	33.39 394	42.05 387	62.48 384	7.00 403	65.56 398	47.08 337	43.21 380	70.27 385

WB-MVS		46.23 359	44.94 361	50.11 379	62.13 393	21.23 412	76.48 359	55.49 398	45.89 381	35.78 391	61.44 387	35.54 342	72.83 388	9.96 406	21.75 401	56.27 394

FPMVS		45.64 360	43.10 364	53.23 377	51.42 402	36.46 395	64.97 386	71.91 378	29.13 397	27.53 397	61.55 386	9.83 399	65.01 400	16.00 403	55.58 355	58.22 393

SSC-MVS		44.51 361	43.35 363	47.99 383	61.01 395	18.90 414	74.12 367	54.36 399	43.42 388	34.10 394	60.02 388	34.42 347	70.39 391	9.14 408	19.57 402	54.68 395

EGC-MVSNET		42.35 362	38.09 365	55.11 373	74.57 364	46.62 373	71.63 372	55.77 397	0.04 411	0.24 412	62.70 383	14.24 394	74.91 386	17.59 400	46.06 375	43.80 397

LCM-MVSNet		40.54 363	35.79 368	54.76 375	36.92 412	30.81 402	51.41 399	69.02 385	22.07 399	24.63 399	45.37 396	4.56 408	65.81 397	33.67 379	34.50 394	67.67 386

APD_test1		40.50 364	37.31 367	50.09 380	51.88 400	35.27 397	59.45 394	52.59 401	21.64 400	26.12 398	57.80 390	4.56 408	66.56 396	22.64 395	39.09 386	48.43 396

test_vis3_rt		40.46 365	37.79 366	48.47 382	44.49 407	33.35 399	66.56 385	32.84 413	32.39 395	29.65 395	39.13 403	3.91 411	68.65 392	50.17 319	40.99 384	43.40 398

ANet_high		40.27 366	35.20 369	55.47 372	34.74 413	34.47 398	63.84 388	71.56 380	48.42 374	18.80 402	41.08 401	9.52 400	64.45 401	20.18 397	8.66 409	67.49 387

test_method		38.59 367	35.16 370	48.89 381	54.33 398	21.35 411	45.32 402	53.71 400	7.41 408	28.74 396	51.62 392	8.70 401	52.87 405	33.73 378	32.89 395	72.47 381

PMMVS2		37.93 368	33.61 371	50.92 378	46.31 404	24.76 408	60.55 393	50.05 402	28.94 398	20.93 400	47.59 393	4.41 410	65.13 399	25.14 393	18.55 404	62.87 390

Gipuma		34.91 369	31.44 372	45.30 384	70.99 376	39.64 392	19.85 406	72.56 376	20.10 402	16.16 406	21.47 407	5.08 407	71.16 390	13.07 404	43.70 379	25.08 404
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 370	29.47 373	42.67 386	41.89 409	30.81 402	52.07 397	43.45 407	15.45 403	18.52 403	44.82 397	2.12 412	58.38 403	16.05 401	30.87 397	38.83 399

APD_test2		32.77 370	29.47 373	42.67 386	41.89 409	30.81 402	52.07 397	43.45 407	15.45 403	18.52 403	44.82 397	2.12 412	58.38 403	16.05 401	30.87 397	38.83 399

PMVS	26.43 22	31.84 372	28.16 375	42.89 385	25.87 415	27.58 406	50.92 400	49.78 403	21.37 401	14.17 407	40.81 402	2.01 414	66.62 395	9.61 407	38.88 388	34.49 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 373	24.00 377	26.45 390	43.74 408	18.44 415	60.86 391	39.66 409	15.11 405	9.53 409	22.10 406	6.52 405	46.94 408	8.31 409	10.14 406	13.98 406

MVE	24.84 23	24.35 374	19.77 380	38.09 388	34.56 414	26.92 407	26.57 404	38.87 411	11.73 407	11.37 408	27.44 404	1.37 415	50.42 407	11.41 405	14.60 405	36.93 401
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 375	23.20 379	25.46 391	41.52 411	16.90 416	60.56 392	38.79 412	14.62 406	8.99 410	20.24 409	7.35 402	45.82 409	7.25 410	9.46 407	13.64 407

tmp_tt		22.26 376	23.75 378	17.80 392	5.23 416	12.06 417	35.26 403	39.48 410	2.82 410	18.94 401	44.20 399	22.23 380	24.64 411	36.30 371	9.31 408	16.69 405

cdsmvs_eth3d_5k		19.86 377	26.47 376	0.00 396	0.00 419	0.00 421	0.00 407	93.45 84	0.00 414	0.00 415	95.27 56	49.56 254	0.00 415	0.00 414	0.00 412	0.00 411

wuyk23d		11.30 378	10.95 381	12.33 393	48.05 403	19.89 413	25.89 405	1.92 417	3.58 409	3.12 411	1.37 411	0.64 416	15.77 412	6.23 411	7.77 410	1.35 408

ab-mvs-re		7.91 379	10.55 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	94.95 66	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

testmvs		7.23 380	9.62 383	0.06 395	0.04 417	0.02 420	84.98 300	0.02 418	0.03 412	0.18 413	1.21 412	0.01 418	0.02 413	0.14 412	0.01 411	0.13 410

test123		6.92 381	9.21 384	0.08 394	0.03 418	0.05 419	81.65 327	0.01 419	0.02 413	0.14 414	0.85 413	0.03 417	0.02 413	0.12 413	0.00 412	0.16 409

pcd_1.5k_mvsjas		4.46 382	5.95 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	53.55 219	0.00 415	0.00 414	0.00 412	0.00 411

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

WAC-MVS						49.45 358								31.56 389

FOURS1					93.95 46	61.77 248	93.96 70	91.92 142	62.14 316	86.57 47

MSC_two_6792asdad				89.60 8	97.31 4	73.22 12		95.05 26					99.07 13	92.01 24	94.77 25	96.51 23

PC_three_1452									80.91 46	94.07 2	96.83 18	83.57 4	99.12 5	95.70 7	97.42 4	97.55 4

No_MVS				89.60 8	97.31 4	73.22 12		95.05 26					99.07 13	92.01 24	94.77 25	96.51 23

test_one_0601					96.32 18	69.74 49		94.18 57	71.42 223	90.67 18	96.85 16	74.45 18

eth-test2					0.00 419

eth-test					0.00 419

ZD-MVS					96.63 9	65.50 154		93.50 82	70.74 238	85.26 62	95.19 62	64.92 81	97.29 77	87.51 57	93.01 56

RE-MVS-def			80.48 140		92.02 97	58.56 302	90.90 207	90.45 202	62.76 309	78.89 124	94.46 80	49.30 257		78.77 132	86.77 127	92.28 179

IU-MVS					96.46 11	69.91 43		95.18 20	80.75 47	95.28 1				92.34 21	95.36 13	96.47 27

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 5				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

test_241102_TWO								94.41 48	71.65 212	92.07 8	97.21 4	74.58 17	99.11 6	92.34 21	95.36 13	96.59 18

test_241102_ONE					96.45 12	69.38 54		94.44 46	71.65 212	92.11 6	97.05 7	76.79 9	99.11 6

9.14			87.63 28		93.86 48		94.41 53	94.18 57	72.76 177	86.21 49	96.51 24	66.64 63	97.88 44	90.08 38	94.04 38

save fliter					93.84 49	67.89 92	95.05 39	92.66 115	78.19 90

test_0728_THIRD									72.48 182	90.55 19	96.93 11	76.24 11	99.08 11	91.53 29	94.99 17	96.43 29

test_0728_SECOND				88.70 17	96.45 12	70.43 35	96.64 9	94.37 52					99.15 2	91.91 27	94.90 21	96.51 23

test0726					96.40 15	69.99 39	96.76 7	94.33 54	71.92 198	91.89 10	97.11 6	73.77 21

GSMVS																94.68 96

test_part2					96.29 19	68.16 86				90.78 16

sam_mvs1												57.85 165				94.68 96

sam_mvs												54.91 204

ambc				69.61 349	61.38 394	41.35 386	49.07 401	85.86 326		50.18 364	66.40 375	10.16 398	88.14 333	45.73 343	44.20 377	79.32 362

MTGPA								92.23 128

test_post1							78.95 347				20.70 408	53.05 224	91.50 304	60.43 282

test_post											23.01 405	56.49 186	92.67 269

patchmatchnet-post											67.62 374	57.62 168	90.25 313

GG-mvs-BLEND				86.53 71	91.91 104	69.67 52	75.02 366	94.75 33		78.67 131	90.85 163	77.91 7	94.56 202	72.25 179	93.74 44	95.36 62

MTMP							93.77 84	32.52 414

gm-plane-assit					88.42 186	67.04 115			78.62 87		91.83 146		97.37 71	76.57 144

test9_res														89.41 40	94.96 18	95.29 67

TEST9					94.18 41	67.28 107	94.16 59	93.51 80	71.75 209	85.52 57	95.33 51	68.01 52	97.27 81

test_8					94.19 40	67.19 109	94.15 61	93.42 86	71.87 203	85.38 60	95.35 50	68.19 50	96.95 104

agg_prior2														86.41 69	94.75 29	95.33 63

agg_prior					94.16 43	66.97 117		93.31 89		84.49 68			96.75 114

TestCases				72.46 336	79.57 324	51.42 348		68.61 386	51.25 365	45.88 374	81.23 295	19.86 386	86.58 347	38.98 367	57.01 351	79.39 360

test_prior4						67.18 111	93.92 73

test_prior2							95.10 38		75.40 130	85.25 63	95.61 45	67.94 53		87.47 58	94.77 25

test_prior				86.42 74	94.71 35	67.35 106		93.10 99					96.84 111			95.05 79

旧先验2							92.00 160		59.37 336	87.54 40			93.47 245	75.39 153

新几何2							91.41 182

新几何1				84.73 131	92.32 89	64.28 185		91.46 168	59.56 335	79.77 113	92.90 120	56.95 178	96.57 119	63.40 262	92.91 58	93.34 146

旧先验1					91.94 101	60.74 270		91.50 166			94.36 84	65.23 76			91.84 72	94.55 103

无先验							92.71 125	92.61 119	62.03 317				97.01 94	66.63 233		93.97 128

原ACMM2							92.01 157

原ACMM1				84.42 145	93.21 66	64.27 186		93.40 88	65.39 287	79.51 116	92.50 128	58.11 164	96.69 115	65.27 252	93.96 39	92.32 177

test222					89.77 151	61.60 253	89.55 245	89.42 247	56.83 349	77.28 144	92.43 132	52.76 227			91.14 86	93.09 154

testdata2													96.09 137	61.26 278

segment_acmp												65.94 69

testdata				81.34 225	89.02 172	57.72 309		89.84 231	58.65 339	85.32 61	94.09 96	57.03 173	93.28 247	69.34 206	90.56 92	93.03 157

testdata1							89.21 254		77.55 103

test12				87.09 51	94.60 36	68.86 66		92.91 105		82.67 84		65.44 74	97.55 62		93.69 47	94.84 89

plane_prior7					86.94 226	61.51 254

plane_prior6					87.23 218	62.32 238						50.66 244

plane_prior5								91.31 172					95.55 167	76.74 142	78.53 201	88.39 241

plane_prior4											89.14 193

plane_prior3						61.95 246			79.09 78	72.53 194

plane_prior2							93.13 109		78.81 84

plane_prior1					87.15 220

plane_prior						62.42 234	93.85 77		79.38 70						78.80 198

n2								0.00 420

nn								0.00 420

door-mid								66.01 390

lessismore_v0				73.72 327	72.93 371	47.83 366		61.72 395		45.86 376	73.76 354	28.63 368	89.81 321	47.75 335	31.37 396	83.53 315

LGP-MVS_train				79.56 272	84.31 274	59.37 291		89.73 237	69.49 251	64.86 282	88.42 198	38.65 317	94.30 211	72.56 176	72.76 244	85.01 301

test11								93.01 101

door								66.57 389

HQP5-MVS						63.66 204

HQP-NCC					87.54 211		94.06 63		79.80 61	74.18 173

ACMP_Plane					87.54 211		94.06 63		79.80 61	74.18 173

BP-MVS														77.63 139

HQP4-MVS										74.18 173			95.61 161			88.63 235

HQP3-MVS								91.70 158							78.90 196

HQP2-MVS												51.63 237

NP-MVS					87.41 214	63.04 219					90.30 173

MDTV_nov1_ep13_2view						59.90 284	80.13 342		67.65 271	72.79 189		54.33 212		59.83 286		92.58 169

MDTV_nov1_ep13			72.61 256		89.06 171	68.48 74	80.33 338	90.11 221	71.84 205	71.81 205	75.92 348	53.01 225	93.92 234	48.04 330	73.38 238

ACMMP++_ref															71.63 252

ACMMP++															69.72 261

Test By Simon												54.21 213

ITE_SJBPF				70.43 347	74.44 365	47.06 372		77.32 363	60.16 331	54.04 348	83.53 264	23.30 378	84.01 360	43.07 351	61.58 332	80.21 357

DeepMVS_CX				34.71 389	51.45 401	24.73 409		28.48 415	31.46 396	17.49 405	52.75 391	5.80 406	42.60 410	18.18 398	19.42 403	36.81 402

High-resolution multi-view benchmark