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 5	97.66 2	73.37 11	97.13 4	95.58 11	89.33 1	85.77 57	96.26 32	72.84 26	99.38 1	92.64 19	95.93 9	97.08 12

MM		90.87 2	91.52 2	88.92 16	92.12 97	71.10 28	97.02 5	96.04 6	88.70 2	91.57 14	96.19 35	70.12 39	98.91 17	96.83 1	95.06 16	96.76 17

DPM-MVS		90.70 3	90.52 8	91.24 1	89.68 154	76.68 2	97.29 2	95.35 15	82.87 22	91.58 13	97.22 3	79.93 5	99.10 9	83.12 100	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 17	97.31 4	69.91 44	93.96 74	94.37 52	72.48 186	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 89	92.83 78	64.03 192	93.06 115	94.33 54	82.19 30	93.65 3	96.15 37	85.89 1	97.19 86	91.02 33	97.75 1	96.43 32
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 23	96.76 8	70.65 32	96.47 15	94.83 30	84.83 13	89.07 35	96.80 19	70.86 35	99.06 15	92.64 19	95.71 10	96.12 41

iter_conf05_11		90.28 7	90.32 11	90.15 2	94.06 45	75.87 5	97.43 1	93.31 89	87.80 4	91.61 12	96.57 23	66.22 66	97.38 70	90.88 34	94.97 18	97.22 8

DELS-MVS		90.05 8	90.09 12	89.94 5	93.14 72	73.88 10	97.01 6	94.40 50	88.32 3	85.71 58	94.91 72	74.11 19	98.91 17	87.26 64	95.94 8	97.03 13
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 9	90.50 9	88.53 24	90.14 145	70.94 29	96.47 15	95.72 10	87.33 5	89.60 32	96.26 32	68.44 45	98.74 24	95.82 4	94.72 32	95.90 48

SED-MVS		89.94 10	90.36 10	88.70 19	96.45 12	69.38 55	96.89 7	94.44 46	71.65 216	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 11	91.38 4	84.72 133	93.00 75	58.16 306	96.72 10	94.41 48	86.50 9	90.25 25	97.83 1	75.46 14	98.67 25	92.78 18	95.49 12	97.32 6

patch_mono-2		89.71 12	90.99 6	85.85 92	96.04 24	63.70 202	95.04 43	95.19 19	86.74 8	91.53 15	95.15 65	73.86 20	97.58 59	93.38 14	92.00 71	96.28 38

CANet		89.61 13	89.99 13	88.46 25	94.39 39	69.71 51	96.53 14	93.78 66	86.89 7	89.68 31	95.78 42	65.94 70	99.10 9	92.99 16	93.91 44	96.58 23

DVP-MVS		89.41 14	89.73 15	88.45 26	96.40 15	69.99 40	96.64 11	94.52 42	71.92 202	90.55 21	96.93 11	73.77 21	99.08 11	91.91 27	94.90 22	96.29 36
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 15	89.95 14	87.64 36	95.10 30	68.23 86	95.24 35	94.49 44	82.43 27	88.90 36	96.35 28	71.89 33	98.63 26	88.76 51	96.40 6	96.06 42

bld_raw_dy_0_64		89.23 16	89.56 16	88.21 29	93.91 50	70.09 38	97.16 3	93.13 98	82.64 25	90.75 18	96.28 31	68.30 49	97.37 72	89.84 40	94.07 40	97.17 9

NCCC		89.07 17	89.46 17	87.91 30	96.60 10	69.05 63	96.38 17	94.64 39	84.42 14	86.74 49	96.20 34	66.56 65	98.76 23	89.03 50	94.56 34	95.92 47

MVSMamba_pp		88.94 18	88.82 19	89.29 14	94.04 46	74.01 9	94.81 50	92.74 113	85.13 11	90.37 23	90.13 183	68.40 47	97.38 70	89.42 42	94.34 37	96.47 30

DPE-MVS		88.77 19	89.21 18	87.45 45	96.26 20	67.56 102	94.17 62	94.15 59	68.77 265	90.74 19	97.27 2	76.09 12	98.49 29	90.58 38	94.91 21	96.30 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mamv4		88.66 20	88.41 22	89.39 13	94.02 47	74.04 8	94.94 47	92.69 116	80.90 49	90.32 24	90.30 176	68.33 48	97.28 83	89.47 41	94.74 31	96.84 16

SMA-MVS		88.14 21	88.29 25	87.67 35	93.21 69	68.72 71	93.85 81	94.03 62	74.18 149	91.74 11	96.67 21	65.61 74	98.42 33	89.24 47	96.08 7	95.88 49
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 21	87.61 32	89.71 7	92.06 98	76.72 1	95.75 22	93.26 91	83.86 16	89.55 33	96.06 38	53.55 217	97.89 43	91.10 31	93.31 55	94.54 107

TSAR-MVS + MP.		88.11 23	88.64 20	86.54 72	91.73 111	68.04 90	90.36 229	93.55 79	82.89 21	91.29 16	92.89 123	72.27 30	96.03 144	87.99 55	94.77 26	95.54 58
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 24	88.37 24	86.70 65	93.51 63	65.32 157	95.15 38	93.84 65	78.17 95	85.93 56	94.80 75	75.80 13	98.21 34	89.38 44	88.78 105	96.59 21

DeepC-MVS_fast	79.48 2	87.95 25	88.00 28	87.79 33	95.86 27	68.32 80	95.74 23	94.11 60	83.82 17	83.49 79	96.19 35	64.53 88	98.44 31	83.42 99	94.88 25	96.61 20
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 26	87.38 36	89.55 12	91.41 122	76.43 3	95.74 23	93.12 100	83.53 19	89.55 33	95.95 40	53.45 221	97.68 50	91.07 32	92.62 62	94.54 107

EPNet		87.84 27	88.38 23	86.23 82	93.30 66	66.05 139	95.26 34	94.84 29	87.09 6	88.06 38	94.53 81	66.79 62	97.34 76	83.89 96	91.68 76	95.29 70
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 28	87.77 30	87.63 40	89.24 169	71.18 25	96.57 13	92.90 108	82.70 24	87.13 44	95.27 58	64.99 79	95.80 149	89.34 45	91.80 74	95.93 46

test_fmvsm_n_1920		87.69 29	88.50 21	85.27 113	87.05 225	63.55 209	93.69 91	91.08 189	84.18 15	90.17 27	97.04 8	67.58 57	97.99 39	95.72 5	90.03 96	94.26 115

APDe-MVS		87.54 30	87.84 29	86.65 66	96.07 23	66.30 135	94.84 49	93.78 66	69.35 256	88.39 37	96.34 29	67.74 56	97.66 54	90.62 37	93.44 53	96.01 45
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 31	88.19 26	85.39 107	86.95 226	64.37 182	94.30 59	88.45 290	80.51 53	92.70 4	96.86 15	69.98 40	97.15 90	95.83 3	88.08 112	94.65 101

SD-MVS		87.49 31	87.49 34	87.50 44	93.60 58	68.82 69	93.90 78	92.63 121	76.86 114	87.90 39	95.76 43	66.17 67	97.63 56	89.06 49	91.48 80	96.05 43
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 33	88.15 27	85.30 111	87.10 223	64.19 189	94.41 57	88.14 299	80.24 61	92.54 5	96.97 10	69.52 42	97.17 87	95.89 2	88.51 108	94.56 104

dcpmvs_2		87.37 34	87.55 33	86.85 58	95.04 32	68.20 87	90.36 229	90.66 201	79.37 75	81.20 95	93.67 107	74.73 15	96.55 123	90.88 34	92.00 71	95.82 50

alignmvs		87.28 35	86.97 40	88.24 28	91.30 124	71.14 27	95.61 27	93.56 78	79.30 76	87.07 46	95.25 60	68.43 46	96.93 109	87.87 56	84.33 147	96.65 19

train_agg		87.21 36	87.42 35	86.60 68	94.18 41	67.28 109	94.16 63	93.51 80	71.87 207	85.52 60	95.33 53	68.19 51	97.27 84	89.09 48	94.90 22	95.25 76

MG-MVS		87.11 37	86.27 47	89.62 8	97.79 1	76.27 4	94.96 46	94.49 44	78.74 90	83.87 78	92.94 121	64.34 89	96.94 107	75.19 156	94.09 39	95.66 53

SF-MVS		87.03 38	87.09 38	86.84 59	92.70 84	67.45 107	93.64 94	93.76 69	70.78 240	86.25 51	96.44 27	66.98 60	97.79 47	88.68 52	94.56 34	95.28 72

CSCG		86.87 39	86.26 48	88.72 18	95.05 31	70.79 31	93.83 86	95.33 16	68.48 269	77.63 142	94.35 90	73.04 24	98.45 30	84.92 86	93.71 49	96.92 15

sasdasda		86.85 40	86.25 49	88.66 21	91.80 109	71.92 17	93.54 99	91.71 158	80.26 58	87.55 41	95.25 60	63.59 102	96.93 109	88.18 53	84.34 145	97.11 10

canonicalmvs		86.85 40	86.25 49	88.66 21	91.80 109	71.92 17	93.54 99	91.71 158	80.26 58	87.55 41	95.25 60	63.59 102	96.93 109	88.18 53	84.34 145	97.11 10

PHI-MVS		86.83 42	86.85 44	86.78 63	93.47 64	65.55 153	95.39 32	95.10 22	71.77 212	85.69 59	96.52 24	62.07 122	98.77 22	86.06 76	95.60 11	96.03 44

SteuartSystems-ACMMP		86.82 43	86.90 42	86.58 70	90.42 139	66.38 132	96.09 19	93.87 64	77.73 102	84.01 77	95.66 45	63.39 105	97.94 40	87.40 62	93.55 52	95.42 59
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 44	86.86 43	86.31 81	93.76 53	67.53 104	96.33 18	93.61 76	82.34 29	81.00 100	93.08 117	63.19 109	97.29 79	87.08 67	91.38 82	94.13 122

testing11		86.71 45	86.44 46	87.55 42	93.54 61	71.35 22	93.65 93	95.58 11	81.36 43	80.69 103	92.21 141	72.30 29	96.46 128	85.18 82	83.43 154	94.82 94

test_fmvsmconf_n		86.58 46	87.17 37	84.82 126	85.28 257	62.55 233	94.26 61	89.78 235	83.81 18	87.78 40	96.33 30	65.33 76	96.98 102	94.40 11	87.55 117	94.95 86

jason		86.40 47	86.17 51	87.11 52	86.16 242	70.54 34	95.71 26	92.19 136	82.00 32	84.58 70	94.34 91	61.86 124	95.53 169	87.76 57	90.89 88	95.27 73
jason: jason.
fmvsm_s_conf0.5_n		86.39 48	86.91 41	84.82 126	87.36 218	63.54 210	94.74 52	90.02 228	82.52 26	90.14 28	96.92 13	62.93 114	97.84 46	95.28 8	82.26 164	93.07 158

WTY-MVS		86.32 49	85.81 58	87.85 31	92.82 80	69.37 57	95.20 36	95.25 17	82.71 23	81.91 90	94.73 76	67.93 55	97.63 56	79.55 126	82.25 165	96.54 24

MSLP-MVS++		86.27 50	85.91 57	87.35 47	92.01 101	68.97 66	95.04 43	92.70 114	79.04 85	81.50 93	96.50 26	58.98 156	96.78 115	83.49 98	93.93 43	96.29 36

VNet		86.20 51	85.65 62	87.84 32	93.92 49	69.99 40	95.73 25	95.94 7	78.43 92	86.00 55	93.07 118	58.22 161	97.00 98	85.22 80	84.33 147	96.52 25

MVS_111021_HR		86.19 52	85.80 59	87.37 46	93.17 71	69.79 48	93.99 73	93.76 69	79.08 83	78.88 129	93.99 101	62.25 121	98.15 36	85.93 77	91.15 86	94.15 121

CS-MVS-test		86.14 53	87.01 39	83.52 171	92.63 86	59.36 294	95.49 29	91.92 145	80.09 62	85.46 62	95.53 49	61.82 126	95.77 152	86.77 71	93.37 54	95.41 60

ACMMP_NAP		86.05 54	85.80 59	86.80 62	91.58 115	67.53 104	91.79 173	93.49 83	74.93 140	84.61 69	95.30 55	59.42 150	97.92 41	86.13 74	94.92 20	94.94 87

testing99		86.01 55	85.47 63	87.63 40	93.62 57	71.25 24	93.47 105	95.23 18	80.42 56	80.60 105	91.95 145	71.73 34	96.50 126	80.02 123	82.22 166	95.13 79

ETV-MVS		86.01 55	86.11 52	85.70 99	90.21 144	67.02 118	93.43 107	91.92 145	81.21 45	84.13 76	94.07 100	60.93 134	95.63 160	89.28 46	89.81 97	94.46 113

testing91		85.93 57	85.31 66	87.78 34	93.59 59	71.47 20	93.50 102	95.08 25	80.26 58	80.53 106	91.93 146	70.43 37	96.51 125	80.32 121	82.13 168	95.37 63

APD-MVS		85.93 57	85.99 55	85.76 96	95.98 26	65.21 160	93.59 97	92.58 123	66.54 282	86.17 53	95.88 41	63.83 95	97.00 98	86.39 73	92.94 59	95.06 81
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 59	85.46 64	87.18 50	88.20 197	72.42 16	92.41 145	92.77 111	82.11 31	80.34 109	93.07 118	68.27 50	95.02 183	78.39 138	93.59 51	94.09 124

CS-MVS		85.80 60	86.65 45	83.27 179	92.00 102	58.92 299	95.31 33	91.86 150	79.97 63	84.82 68	95.40 51	62.26 120	95.51 170	86.11 75	92.08 70	95.37 63

fmvsm_s_conf0.5_n_a		85.75 61	86.09 53	84.72 133	85.73 251	63.58 207	93.79 87	89.32 253	81.42 41	90.21 26	96.91 14	62.41 119	97.67 51	94.48 10	80.56 183	92.90 164

test_fmvsmconf0.1_n		85.71 62	86.08 54	84.62 140	80.83 309	62.33 238	93.84 84	88.81 278	83.50 20	87.00 47	96.01 39	63.36 106	96.93 109	94.04 12	87.29 120	94.61 103

CDPH-MVS		85.71 62	85.46 64	86.46 74	94.75 34	67.19 111	93.89 79	92.83 110	70.90 236	83.09 82	95.28 56	63.62 100	97.36 74	80.63 118	94.18 38	94.84 91

casdiffmvs_mvg		85.66 64	85.18 68	87.09 53	88.22 196	69.35 58	93.74 90	91.89 148	81.47 37	80.10 111	91.45 155	64.80 84	96.35 129	87.23 65	87.69 115	95.58 56
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 65	85.93 56	84.68 136	82.95 294	63.48 212	94.03 72	89.46 247	81.69 35	89.86 29	96.74 20	61.85 125	97.75 49	94.74 9	82.01 170	92.81 166

MGCFI-Net		85.59 66	85.73 61	85.17 117	91.41 122	62.44 234	92.87 123	91.31 175	79.65 69	86.99 48	95.14 66	62.90 115	96.12 136	87.13 66	84.13 152	96.96 14

DeepC-MVS	77.85 3	85.52 67	85.24 67	86.37 78	88.80 179	66.64 126	92.15 152	93.68 74	81.07 46	76.91 152	93.64 108	62.59 117	98.44 31	85.50 78	92.84 61	94.03 128
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 68	84.87 74	86.84 59	88.25 194	69.07 62	93.04 117	91.76 155	81.27 44	80.84 102	92.07 143	64.23 90	96.06 142	84.98 85	87.43 119	95.39 61
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 69	85.08 70	86.06 84	93.09 74	65.65 149	93.89 79	93.41 87	73.75 160	79.94 113	94.68 78	60.61 137	98.03 38	82.63 103	93.72 48	94.52 109

MP-MVS-pluss		85.24 70	85.13 69	85.56 102	91.42 120	65.59 151	91.54 183	92.51 125	74.56 143	80.62 104	95.64 46	59.15 154	97.00 98	86.94 69	93.80 45	94.07 126
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 71	84.69 76	86.63 67	92.91 77	69.91 44	92.61 136	95.80 9	80.31 57	80.38 108	92.27 138	68.73 44	95.19 180	75.94 151	83.27 156	94.81 95

PAPR		85.15 72	84.47 77	87.18 50	96.02 25	68.29 81	91.85 171	93.00 105	76.59 121	79.03 125	95.00 67	61.59 127	97.61 58	78.16 139	89.00 104	95.63 54

MP-MVS		85.02 73	84.97 72	85.17 117	92.60 87	64.27 187	93.24 110	92.27 130	73.13 171	79.63 117	94.43 84	61.90 123	97.17 87	85.00 84	92.56 63	94.06 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 74	84.44 78	86.71 64	88.33 191	68.73 70	90.24 234	91.82 154	81.05 47	81.18 96	92.50 130	63.69 98	96.08 141	84.45 90	86.71 129	95.32 68

CHOSEN 1792x2688		84.98 75	83.45 91	89.57 11	89.94 149	75.14 6	92.07 158	92.32 128	81.87 33	75.68 161	88.27 205	60.18 140	98.60 27	80.46 120	90.27 95	94.96 85

EIA-MVS		84.84 76	84.88 73	84.69 135	91.30 124	62.36 237	93.85 81	92.04 140	79.45 72	79.33 122	94.28 94	62.42 118	96.35 129	80.05 122	91.25 85	95.38 62

fmvsm_s_conf0.1_n_a		84.76 77	84.84 75	84.53 142	80.23 319	63.50 211	92.79 125	88.73 281	80.46 54	89.84 30	96.65 22	60.96 133	97.57 61	93.80 13	80.14 185	92.53 173

HFP-MVS		84.73 78	84.40 79	85.72 98	93.75 55	65.01 166	93.50 102	93.19 95	72.19 196	79.22 123	94.93 70	59.04 155	97.67 51	81.55 109	92.21 66	94.49 112

MVS		84.66 79	82.86 107	90.06 3	90.93 130	74.56 7	87.91 279	95.54 13	68.55 267	72.35 202	94.71 77	59.78 146	98.90 19	81.29 115	94.69 33	96.74 18

GST-MVS		84.63 80	84.29 80	85.66 100	92.82 80	65.27 158	93.04 117	93.13 98	73.20 169	78.89 126	94.18 97	59.41 151	97.85 45	81.45 111	92.48 65	93.86 136

EC-MVSNet		84.53 81	85.04 71	83.01 183	89.34 161	61.37 258	94.42 56	91.09 187	77.91 99	83.24 80	94.20 96	58.37 159	95.40 171	85.35 79	91.41 81	92.27 183

ACMMPR		84.37 82	84.06 81	85.28 112	93.56 60	64.37 182	93.50 102	93.15 97	72.19 196	78.85 131	94.86 73	56.69 181	97.45 65	81.55 109	92.20 67	94.02 129

region2R		84.36 83	84.03 82	85.36 109	93.54 61	64.31 185	93.43 107	92.95 106	72.16 199	78.86 130	94.84 74	56.97 176	97.53 63	81.38 113	92.11 69	94.24 116

LFMVS		84.34 84	82.73 109	89.18 15	94.76 33	73.25 12	94.99 45	91.89 148	71.90 204	82.16 89	93.49 112	47.98 268	97.05 93	82.55 104	84.82 141	97.25 7

test_yl		84.28 85	83.16 100	87.64 36	94.52 37	69.24 59	95.78 20	95.09 23	69.19 259	81.09 97	92.88 124	57.00 174	97.44 66	81.11 116	81.76 172	96.23 39

DCV-MVSNet		84.28 85	83.16 100	87.64 36	94.52 37	69.24 59	95.78 20	95.09 23	69.19 259	81.09 97	92.88 124	57.00 174	97.44 66	81.11 116	81.76 172	96.23 39

diffmvs		84.28 85	83.83 83	85.61 101	87.40 216	68.02 91	90.88 213	89.24 256	80.54 52	81.64 92	92.52 129	59.83 145	94.52 206	87.32 63	85.11 139	94.29 114
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 85	83.36 98	87.02 56	92.22 94	67.74 97	84.65 304	94.50 43	79.15 80	82.23 88	87.93 214	66.88 61	96.94 107	80.53 119	82.20 167	96.39 34

ETVMVS		84.22 89	83.71 84	85.76 96	92.58 88	68.25 85	92.45 144	95.53 14	79.54 71	79.46 119	91.64 153	70.29 38	94.18 219	69.16 209	82.76 162	94.84 91

MAR-MVS		84.18 90	83.43 93	86.44 75	96.25 21	65.93 144	94.28 60	94.27 56	74.41 144	79.16 124	95.61 47	53.99 212	98.88 21	69.62 203	93.26 56	94.50 111
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 91	83.20 99	87.05 55	91.56 116	69.82 47	89.99 243	92.05 139	77.77 101	82.84 83	86.57 233	63.93 94	96.09 138	74.91 161	89.18 103	95.25 76

CANet_DTU		84.09 92	83.52 86	85.81 93	90.30 142	66.82 121	91.87 169	89.01 270	85.27 10	86.09 54	93.74 105	47.71 272	96.98 102	77.90 141	89.78 99	93.65 141

ET-MVSNet_ETH3D		84.01 93	83.15 102	86.58 70	90.78 135	70.89 30	94.74 52	94.62 40	81.44 40	58.19 330	93.64 108	73.64 23	92.35 283	82.66 102	78.66 200	96.50 29

PVSNet_Blended_VisFu		83.97 94	83.50 88	85.39 107	90.02 147	66.59 129	93.77 88	91.73 156	77.43 110	77.08 151	89.81 188	63.77 97	96.97 104	79.67 125	88.21 110	92.60 170

MTAPA		83.91 95	83.38 97	85.50 103	91.89 107	65.16 162	81.75 327	92.23 131	75.32 135	80.53 106	95.21 63	56.06 189	97.16 89	84.86 87	92.55 64	94.18 118

XVS		83.87 96	83.47 90	85.05 119	93.22 67	63.78 196	92.92 121	92.66 118	73.99 152	78.18 135	94.31 93	55.25 195	97.41 68	79.16 129	91.58 78	93.95 131

Effi-MVS+		83.82 97	82.76 108	86.99 57	89.56 157	69.40 54	91.35 195	86.12 323	72.59 183	83.22 81	92.81 127	59.60 148	96.01 146	81.76 108	87.80 114	95.56 57

test_fmvsmvis_n_1920		83.80 98	83.48 89	84.77 130	82.51 296	63.72 200	91.37 193	83.99 343	81.42 41	77.68 141	95.74 44	58.37 159	97.58 59	93.38 14	86.87 123	93.00 161

EI-MVSNet-Vis-set		83.77 99	83.67 85	84.06 158	92.79 83	63.56 208	91.76 176	94.81 31	79.65 69	77.87 139	94.09 98	63.35 107	97.90 42	79.35 127	79.36 192	90.74 209

MVSFormer		83.75 100	82.88 106	86.37 78	89.24 169	71.18 25	89.07 261	90.69 198	65.80 287	87.13 44	94.34 91	64.99 79	92.67 270	72.83 172	91.80 74	95.27 73

CP-MVS		83.71 101	83.40 96	84.65 137	93.14 72	63.84 194	94.59 54	92.28 129	71.03 234	77.41 145	94.92 71	55.21 198	96.19 133	81.32 114	90.70 90	93.91 133

test_fmvsmconf0.01_n		83.70 102	83.52 86	84.25 155	75.26 360	61.72 252	92.17 151	87.24 312	82.36 28	84.91 67	95.41 50	55.60 193	96.83 114	92.85 17	85.87 135	94.21 117

baseline2		83.68 103	83.42 95	84.48 145	87.37 217	66.00 141	90.06 238	95.93 8	79.71 68	69.08 238	90.39 174	77.92 6	96.28 131	78.91 133	81.38 176	91.16 205

iter_conf05		83.65 104	83.44 92	84.28 153	86.17 241	68.61 75	95.08 41	89.82 234	80.90 49	78.08 137	90.49 171	69.08 43	95.22 179	84.29 91	77.07 216	89.02 231

thisisatest0515		83.41 105	82.49 113	86.16 83	89.46 160	68.26 83	93.54 99	94.70 36	74.31 147	75.75 159	90.92 163	72.62 27	96.52 124	69.64 201	81.50 175	93.71 139

PVSNet_BlendedMVS		83.38 106	83.43 93	83.22 180	93.76 53	67.53 104	94.06 67	93.61 76	79.13 81	81.00 100	85.14 248	63.19 109	97.29 79	87.08 67	73.91 237	84.83 305

test2506		83.29 107	82.92 105	84.37 149	88.39 189	63.18 219	92.01 161	91.35 174	77.66 104	78.49 134	91.42 156	64.58 87	95.09 182	73.19 168	89.23 101	94.85 88

PGM-MVS		83.25 108	82.70 110	84.92 122	92.81 82	64.07 191	90.44 225	92.20 135	71.28 228	77.23 148	94.43 84	55.17 199	97.31 78	79.33 128	91.38 82	93.37 147

HPM-MVS		83.25 108	82.95 104	84.17 156	92.25 93	62.88 228	90.91 210	91.86 150	70.30 245	77.12 149	93.96 102	56.75 179	96.28 131	82.04 106	91.34 84	93.34 148
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-UG-set		83.14 110	82.96 103	83.67 169	92.28 92	63.19 218	91.38 192	94.68 37	79.22 78	76.60 154	93.75 104	62.64 116	97.76 48	78.07 140	78.01 203	90.05 218

VDD-MVS		83.06 111	81.81 122	86.81 61	90.86 133	67.70 98	95.40 31	91.50 169	75.46 132	81.78 91	92.34 137	40.09 310	97.13 91	86.85 70	82.04 169	95.60 55

h-mvs33		83.01 112	82.56 112	84.35 150	89.34 161	62.02 244	92.72 128	93.76 69	81.45 38	82.73 85	92.25 140	60.11 141	97.13 91	87.69 58	62.96 314	93.91 133

PAPM_NR		82.97 113	81.84 121	86.37 78	94.10 44	66.76 124	87.66 283	92.84 109	69.96 249	74.07 180	93.57 110	63.10 112	97.50 64	70.66 196	90.58 92	94.85 88

mPP-MVS		82.96 114	82.44 114	84.52 143	92.83 78	62.92 226	92.76 126	91.85 152	71.52 224	75.61 164	94.24 95	53.48 220	96.99 101	78.97 132	90.73 89	93.64 142

SR-MVS		82.81 115	82.58 111	83.50 174	93.35 65	61.16 261	92.23 150	91.28 179	64.48 296	81.27 94	95.28 56	53.71 216	95.86 148	82.87 101	88.77 106	93.49 145

DP-MVS Recon		82.73 116	81.65 123	85.98 86	97.31 4	67.06 115	95.15 38	91.99 142	69.08 262	76.50 156	93.89 103	54.48 207	98.20 35	70.76 194	85.66 137	92.69 167

CLD-MVS		82.73 116	82.35 116	83.86 162	87.90 204	67.65 100	95.45 30	92.18 137	85.06 12	72.58 195	92.27 138	52.46 228	95.78 150	84.18 92	79.06 195	88.16 246
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 118	82.38 115	83.73 166	89.25 166	59.58 289	92.24 149	94.89 28	77.96 97	79.86 114	92.38 135	56.70 180	97.05 93	77.26 144	80.86 180	94.55 105

3Dnovator	73.91 6	82.69 119	80.82 134	88.31 27	89.57 156	71.26 23	92.60 137	94.39 51	78.84 87	67.89 259	92.48 133	48.42 263	98.52 28	68.80 214	94.40 36	95.15 78

MVSTER		82.47 120	82.05 117	83.74 164	92.68 85	69.01 64	91.90 168	93.21 92	79.83 64	72.14 203	85.71 245	74.72 16	94.72 194	75.72 152	72.49 248	87.50 251

TESTMET0.1,1		82.41 121	81.98 120	83.72 167	88.08 198	63.74 198	92.70 130	93.77 68	79.30 76	77.61 143	87.57 220	58.19 162	94.08 223	73.91 167	86.68 130	93.33 150

CostFormer		82.33 122	81.15 127	85.86 91	89.01 174	68.46 77	82.39 324	93.01 103	75.59 130	80.25 110	81.57 289	72.03 32	94.96 186	79.06 131	77.48 211	94.16 120

API-MVS		82.28 123	80.53 141	87.54 43	96.13 22	70.59 33	93.63 95	91.04 193	65.72 289	75.45 166	92.83 126	56.11 188	98.89 20	64.10 258	89.75 100	93.15 154

IB-MVS	77.80 4	82.18 124	80.46 143	87.35 47	89.14 171	70.28 37	95.59 28	95.17 21	78.85 86	70.19 226	85.82 243	70.66 36	97.67 51	72.19 183	66.52 288	94.09 124
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		82.16 125	81.12 128	85.26 114	86.42 234	68.72 71	92.59 139	90.44 208	73.12 172	84.20 73	94.36 86	38.04 323	95.73 154	84.12 93	86.81 124	91.33 198

xiu_mvs_v1_base		82.16 125	81.12 128	85.26 114	86.42 234	68.72 71	92.59 139	90.44 208	73.12 172	84.20 73	94.36 86	38.04 323	95.73 154	84.12 93	86.81 124	91.33 198

xiu_mvs_v1_base_debi		82.16 125	81.12 128	85.26 114	86.42 234	68.72 71	92.59 139	90.44 208	73.12 172	84.20 73	94.36 86	38.04 323	95.73 154	84.12 93	86.81 124	91.33 198

3Dnovator+	73.60 7	82.10 128	80.60 140	86.60 68	90.89 132	66.80 123	95.20 36	93.44 85	74.05 151	67.42 265	92.49 132	49.46 253	97.65 55	70.80 193	91.68 76	95.33 66

MVS_111021_LR		82.02 129	81.52 124	83.51 173	88.42 187	62.88 228	89.77 246	88.93 274	76.78 117	75.55 165	93.10 115	50.31 245	95.38 173	83.82 97	87.02 122	92.26 184

PMMVS		81.98 130	82.04 118	81.78 216	89.76 153	56.17 325	91.13 206	90.69 198	77.96 97	80.09 112	93.57 110	46.33 282	94.99 185	81.41 112	87.46 118	94.17 119

baseline1		81.84 131	81.03 132	84.28 153	91.60 114	66.62 127	91.08 207	91.66 163	81.87 33	74.86 171	91.67 152	69.98 40	94.92 189	71.76 186	64.75 302	91.29 203

EPP-MVSNet		81.79 132	81.52 124	82.61 192	88.77 180	60.21 281	93.02 119	93.66 75	68.52 268	72.90 190	90.39 174	72.19 31	94.96 186	74.93 160	79.29 194	92.67 168

test_vis1_n_1920		81.66 133	82.01 119	80.64 243	82.24 298	55.09 333	94.76 51	86.87 314	81.67 36	84.40 72	94.63 79	38.17 320	94.67 198	91.98 26	83.34 155	92.16 187

APD-MVS_3200maxsize		81.64 134	81.32 126	82.59 193	92.36 90	58.74 301	91.39 190	91.01 194	63.35 305	79.72 116	94.62 80	51.82 231	96.14 135	79.71 124	87.93 113	92.89 165

ACMMP		81.49 135	80.67 137	83.93 161	91.71 112	62.90 227	92.13 153	92.22 134	71.79 211	71.68 210	93.49 112	50.32 244	96.96 105	78.47 137	84.22 151	91.93 190
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		81.43 136	80.74 135	83.52 171	86.26 238	64.45 176	92.09 156	90.65 202	75.83 128	73.95 182	89.81 188	63.97 93	92.91 260	71.27 189	82.82 159	93.20 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 137	79.99 148	85.46 104	90.39 141	68.40 78	86.88 294	90.61 203	74.41 144	70.31 225	84.67 253	63.79 96	92.32 284	73.13 169	85.70 136	95.67 52

ECVR-MVS		81.29 138	80.38 144	84.01 160	88.39 189	61.96 246	92.56 142	86.79 316	77.66 104	76.63 153	91.42 156	46.34 281	95.24 178	74.36 165	89.23 101	94.85 88

thisisatest0530		81.15 139	80.07 145	84.39 148	88.26 193	65.63 150	91.40 188	94.62 40	71.27 229	70.93 216	89.18 194	72.47 28	96.04 143	65.62 247	76.89 218	91.49 194

Fast-Effi-MVS+		81.14 140	80.01 147	84.51 144	90.24 143	65.86 145	94.12 66	89.15 262	73.81 159	75.37 167	88.26 206	57.26 169	94.53 205	66.97 232	84.92 140	93.15 154

HQP-MVS		81.14 140	80.64 138	82.64 191	87.54 212	63.66 205	94.06 67	91.70 161	79.80 65	74.18 176	90.30 176	51.63 235	95.61 162	77.63 142	78.90 196	88.63 237

hse-mvs2		81.12 142	81.11 131	81.16 230	86.52 233	57.48 314	89.40 254	91.16 182	81.45 38	82.73 85	90.49 171	60.11 141	94.58 199	87.69 58	60.41 341	91.41 197

SR-MVS-dyc-post		81.06 143	80.70 136	82.15 207	92.02 99	58.56 303	90.90 211	90.45 205	62.76 312	78.89 126	94.46 82	51.26 239	95.61 162	78.77 135	86.77 127	92.28 180

HyFIR lowres test		81.03 144	79.56 155	85.43 105	87.81 208	68.11 89	90.18 235	90.01 229	70.65 242	72.95 189	86.06 241	63.61 101	94.50 207	75.01 159	79.75 189	93.67 140

nrg030		80.93 145	79.86 150	84.13 157	83.69 283	68.83 68	93.23 111	91.20 180	75.55 131	75.06 169	88.22 209	63.04 113	94.74 193	81.88 107	66.88 285	88.82 235

Vis-MVSNet		80.92 146	79.98 149	83.74 164	88.48 184	61.80 248	93.44 106	88.26 298	73.96 155	77.73 140	91.76 149	49.94 249	94.76 191	65.84 244	90.37 94	94.65 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 147	80.02 146	83.33 177	87.87 205	60.76 269	92.62 135	86.86 315	77.86 100	75.73 160	91.39 158	46.35 280	94.70 197	72.79 174	88.68 107	94.52 109

UWE-MVS		80.81 148	81.01 133	80.20 253	89.33 163	57.05 319	91.91 167	94.71 35	75.67 129	75.01 170	89.37 192	63.13 111	91.44 306	67.19 229	82.80 161	92.12 188

1314		80.70 149	78.95 166	85.94 88	87.77 210	67.56 102	87.91 279	92.55 124	72.17 198	67.44 264	93.09 116	50.27 246	97.04 96	71.68 188	87.64 116	93.23 152

tpmrst		80.57 150	79.14 165	84.84 125	90.10 146	68.28 82	81.70 328	89.72 242	77.63 106	75.96 158	79.54 321	64.94 81	92.71 267	75.43 154	77.28 214	93.55 143

1112_ss		80.56 151	79.83 151	82.77 187	88.65 181	60.78 267	92.29 147	88.36 292	72.58 184	72.46 199	94.95 68	65.09 78	93.42 247	66.38 238	77.71 205	94.10 123

VDDNet		80.50 152	78.26 174	87.21 49	86.19 239	69.79 48	94.48 55	91.31 175	60.42 330	79.34 121	90.91 164	38.48 318	96.56 122	82.16 105	81.05 178	95.27 73

BH-w/o		80.49 153	79.30 162	84.05 159	90.83 134	64.36 184	93.60 96	89.42 250	74.35 146	69.09 237	90.15 182	55.23 197	95.61 162	64.61 255	86.43 133	92.17 186

test_cas_vis1_n_1920		80.45 154	80.61 139	79.97 262	78.25 345	57.01 321	94.04 71	88.33 293	79.06 84	82.81 84	93.70 106	38.65 315	91.63 298	90.82 36	79.81 187	91.27 204

TAMVS		80.37 155	79.45 158	83.13 182	85.14 260	63.37 213	91.23 201	90.76 197	74.81 142	72.65 193	88.49 200	60.63 136	92.95 255	69.41 205	81.95 171	93.08 157

HQP_MVS		80.34 156	79.75 152	82.12 209	86.94 227	62.42 235	93.13 113	91.31 175	78.81 88	72.53 196	89.14 196	50.66 242	95.55 167	76.74 145	78.53 201	88.39 243

SDMVSNet		80.26 157	78.88 167	84.40 147	89.25 166	67.63 101	85.35 300	93.02 102	76.77 118	70.84 217	87.12 227	47.95 269	96.09 138	85.04 83	74.55 228	89.48 228

HPM-MVS_fast		80.25 158	79.55 157	82.33 199	91.55 117	59.95 284	91.32 197	89.16 261	65.23 293	74.71 173	93.07 118	47.81 271	95.74 153	74.87 163	88.23 109	91.31 202

ab-mvs		80.18 159	78.31 173	85.80 94	88.44 186	65.49 156	83.00 321	92.67 117	71.82 210	77.36 146	85.01 249	54.50 204	96.59 119	76.35 149	75.63 225	95.32 68

IS-MVSNet		80.14 160	79.41 159	82.33 199	87.91 203	60.08 283	91.97 165	88.27 296	72.90 179	71.44 213	91.73 151	61.44 128	93.66 242	62.47 272	86.53 131	93.24 151

test-LLR		80.10 161	79.56 155	81.72 218	86.93 229	61.17 259	92.70 130	91.54 166	71.51 225	75.62 162	86.94 229	53.83 213	92.38 280	72.21 181	84.76 143	91.60 192

PVSNet	73.49 8	80.05 162	78.63 169	84.31 151	90.92 131	64.97 167	92.47 143	91.05 192	79.18 79	72.43 200	90.51 170	37.05 335	94.06 225	68.06 218	86.00 134	93.90 135

UA-Net		80.02 163	79.65 153	81.11 232	89.33 163	57.72 310	86.33 297	89.00 273	77.44 109	81.01 99	89.15 195	59.33 152	95.90 147	61.01 279	84.28 149	89.73 224

test-mter		79.96 164	79.38 161	81.72 218	86.93 229	61.17 259	92.70 130	91.54 166	73.85 157	75.62 162	86.94 229	49.84 251	92.38 280	72.21 181	84.76 143	91.60 192

QAPM		79.95 165	77.39 191	87.64 36	89.63 155	71.41 21	93.30 109	93.70 73	65.34 292	67.39 267	91.75 150	47.83 270	98.96 16	57.71 295	89.81 97	92.54 172

UGNet		79.87 166	78.68 168	83.45 176	89.96 148	61.51 255	92.13 153	90.79 196	76.83 116	78.85 131	86.33 237	38.16 321	96.17 134	67.93 221	87.17 121	92.67 168
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		79.80 167	77.95 180	85.34 110	88.28 192	68.26 83	81.56 330	91.42 172	70.11 247	77.59 144	80.50 307	67.40 58	94.26 216	67.34 226	77.35 212	93.51 144

thres200		79.66 168	78.33 172	83.66 170	92.54 89	65.82 147	93.06 115	96.31 3	74.90 141	73.30 186	88.66 198	59.67 147	95.61 162	47.84 333	78.67 199	89.56 227

CPTT-MVS		79.59 169	79.16 164	80.89 241	91.54 118	59.80 286	92.10 155	88.54 289	60.42 330	72.96 188	93.28 114	48.27 264	92.80 264	78.89 134	86.50 132	90.06 217

Test_1112_low_res		79.56 170	78.60 170	82.43 195	88.24 195	60.39 278	92.09 156	87.99 303	72.10 200	71.84 206	87.42 222	64.62 86	93.04 251	65.80 245	77.30 213	93.85 137

tttt0517		79.50 171	78.53 171	82.41 198	87.22 220	61.43 257	89.75 247	94.76 32	69.29 257	67.91 257	88.06 213	72.92 25	95.63 160	62.91 268	73.90 238	90.16 216

FIs		79.47 172	79.41 159	79.67 269	85.95 245	59.40 291	91.68 180	93.94 63	78.06 96	68.96 242	88.28 204	66.61 64	91.77 295	66.20 241	74.99 227	87.82 248

BH-RMVSNet		79.46 173	77.65 183	84.89 123	91.68 113	65.66 148	93.55 98	88.09 301	72.93 176	73.37 185	91.12 162	46.20 284	96.12 136	56.28 300	85.61 138	92.91 163

PCF-MVS	73.15 9	79.29 174	77.63 184	84.29 152	86.06 243	65.96 143	87.03 290	91.10 186	69.86 251	69.79 233	90.64 166	57.54 168	96.59 119	64.37 257	82.29 163	90.32 214
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 175	79.57 154	78.24 289	88.46 185	52.29 344	90.41 227	89.12 264	74.24 148	69.13 236	91.91 147	65.77 72	90.09 321	59.00 291	88.09 111	92.33 177

114514_t		79.17 176	77.67 182	83.68 168	95.32 29	65.53 154	92.85 124	91.60 165	63.49 303	67.92 256	90.63 168	46.65 277	95.72 158	67.01 231	83.54 153	89.79 222

FA-MVS(test-final)		79.12 177	77.23 193	84.81 129	90.54 137	63.98 193	81.35 333	91.71 158	71.09 233	74.85 172	82.94 270	52.85 224	97.05 93	67.97 219	81.73 174	93.41 146

VPA-MVSNet		79.03 178	78.00 178	82.11 212	85.95 245	64.48 175	93.22 112	94.66 38	75.05 139	74.04 181	84.95 250	52.17 230	93.52 244	74.90 162	67.04 284	88.32 245

OPM-MVS		79.00 179	78.09 176	81.73 217	83.52 286	63.83 195	91.64 182	90.30 215	76.36 124	71.97 205	89.93 187	46.30 283	95.17 181	75.10 157	77.70 206	86.19 278
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 180	78.22 175	81.25 227	85.33 255	62.73 231	89.53 251	93.21 92	72.39 191	72.14 203	90.13 183	60.99 131	94.72 194	67.73 223	72.49 248	86.29 275

AdaColmap		78.94 181	77.00 197	84.76 131	96.34 17	65.86 145	92.66 134	87.97 305	62.18 317	70.56 219	92.37 136	43.53 298	97.35 75	64.50 256	82.86 158	91.05 207

GeoE		78.90 182	77.43 187	83.29 178	88.95 175	62.02 244	92.31 146	86.23 321	70.24 246	71.34 214	89.27 193	54.43 208	94.04 228	63.31 264	80.81 182	93.81 138

miper_enhance_ethall		78.86 183	77.97 179	81.54 222	88.00 202	65.17 161	91.41 186	89.15 262	75.19 137	68.79 245	83.98 261	67.17 59	92.82 262	72.73 175	65.30 293	86.62 272

VPNet		78.82 184	77.53 186	82.70 189	84.52 270	66.44 131	93.93 76	92.23 131	80.46 54	72.60 194	88.38 203	49.18 257	93.13 250	72.47 179	63.97 311	88.55 240

EPNet_dtu		78.80 185	79.26 163	77.43 297	88.06 199	49.71 357	91.96 166	91.95 144	77.67 103	76.56 155	91.28 160	58.51 158	90.20 319	56.37 299	80.95 179	92.39 175
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 186	77.43 187	82.88 185	92.21 95	64.49 173	92.05 159	96.28 4	73.48 166	71.75 208	88.26 206	60.07 143	95.32 174	45.16 344	77.58 208	88.83 233

TR-MVS		78.77 187	77.37 192	82.95 184	90.49 138	60.88 265	93.67 92	90.07 224	70.08 248	74.51 174	91.37 159	45.69 287	95.70 159	60.12 285	80.32 184	92.29 179

thres400		78.68 188	77.43 187	82.43 195	92.21 95	64.49 173	92.05 159	96.28 4	73.48 166	71.75 208	88.26 206	60.07 143	95.32 174	45.16 344	77.58 208	87.48 252

BH-untuned		78.68 188	77.08 194	83.48 175	89.84 150	63.74 198	92.70 130	88.59 287	71.57 222	66.83 274	88.65 199	51.75 233	95.39 172	59.03 290	84.77 142	91.32 201

OMC-MVS		78.67 190	77.91 181	80.95 239	85.76 250	57.40 316	88.49 270	88.67 284	73.85 157	72.43 200	92.10 142	49.29 256	94.55 204	72.73 175	77.89 204	90.91 208

tpm		78.58 191	77.03 195	83.22 180	85.94 247	64.56 171	83.21 318	91.14 185	78.31 93	73.67 183	79.68 319	64.01 92	92.09 289	66.07 242	71.26 258	93.03 159

OpenMVS	70.45 11	78.54 192	75.92 211	86.41 77	85.93 248	71.68 19	92.74 127	92.51 125	66.49 283	64.56 288	91.96 144	43.88 297	98.10 37	54.61 305	90.65 91	89.44 230

EPMVS		78.49 193	75.98 210	86.02 85	91.21 126	69.68 52	80.23 342	91.20 180	75.25 136	72.48 198	78.11 329	54.65 203	93.69 241	57.66 296	83.04 157	94.69 97

AUN-MVS		78.37 194	77.43 187	81.17 229	86.60 232	57.45 315	89.46 253	91.16 182	74.11 150	74.40 175	90.49 171	55.52 194	94.57 201	74.73 164	60.43 340	91.48 195

thres100view900		78.37 194	77.01 196	82.46 194	91.89 107	63.21 217	91.19 205	96.33 1	72.28 194	70.45 222	87.89 215	60.31 138	95.32 174	45.16 344	77.58 208	88.83 233

GA-MVS		78.33 196	76.23 206	84.65 137	83.65 284	66.30 135	91.44 184	90.14 222	76.01 126	70.32 224	84.02 260	42.50 302	94.72 194	70.98 191	77.00 217	92.94 162

cascas		78.18 197	75.77 213	85.41 106	87.14 222	69.11 61	92.96 120	91.15 184	66.71 281	70.47 220	86.07 240	37.49 329	96.48 127	70.15 199	79.80 188	90.65 210

UniMVSNet_NR-MVSNet		78.15 198	77.55 185	79.98 260	84.46 272	60.26 279	92.25 148	93.20 94	77.50 108	68.88 243	86.61 232	66.10 68	92.13 287	66.38 238	62.55 318	87.54 250

thres600view7		78.00 199	76.66 201	82.03 214	91.93 104	63.69 203	91.30 198	96.33 1	72.43 189	70.46 221	87.89 215	60.31 138	94.92 189	42.64 356	76.64 219	87.48 252

FC-MVSNet-test		77.99 200	78.08 177	77.70 292	84.89 265	55.51 330	90.27 232	93.75 72	76.87 113	66.80 275	87.59 219	65.71 73	90.23 318	62.89 269	73.94 236	87.37 255

Anonymous202405211		77.96 201	75.33 220	85.87 90	93.73 56	64.52 172	94.85 48	85.36 329	62.52 315	76.11 157	90.18 180	29.43 363	97.29 79	68.51 216	77.24 215	95.81 51

cl22		77.94 202	76.78 199	81.42 224	87.57 211	64.93 169	90.67 220	88.86 277	72.45 188	67.63 263	82.68 274	64.07 91	92.91 260	71.79 184	65.30 293	86.44 273

XXY-MVS		77.94 202	76.44 203	82.43 195	82.60 295	64.44 177	92.01 161	91.83 153	73.59 165	70.00 229	85.82 243	54.43 208	94.76 191	69.63 202	68.02 278	88.10 247

MS-PatchMatch		77.90 204	76.50 202	82.12 209	85.99 244	69.95 43	91.75 178	92.70 114	73.97 154	62.58 309	84.44 257	41.11 307	95.78 150	63.76 261	92.17 68	80.62 352

FMVSNet3		77.73 205	76.04 209	82.80 186	91.20 127	68.99 65	91.87 169	91.99 142	73.35 168	67.04 270	83.19 269	56.62 182	92.14 286	59.80 287	69.34 265	87.28 259

miper_ehance_all_eth		77.60 206	76.44 203	81.09 236	85.70 252	64.41 180	90.65 221	88.64 286	72.31 192	67.37 268	82.52 275	64.77 85	92.64 273	70.67 195	65.30 293	86.24 277

UniMVSNet (Re)		77.58 207	76.78 199	79.98 260	84.11 278	60.80 266	91.76 176	93.17 96	76.56 122	69.93 232	84.78 252	63.32 108	92.36 282	64.89 254	62.51 320	86.78 267

PatchmatchNet		77.46 208	74.63 226	85.96 87	89.55 158	70.35 36	79.97 347	89.55 245	72.23 195	70.94 215	76.91 340	57.03 172	92.79 265	54.27 307	81.17 177	94.74 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 209	75.65 216	82.73 188	80.38 315	67.13 114	91.85 171	90.23 219	75.09 138	69.37 234	83.39 267	53.79 215	94.44 208	71.77 185	65.00 299	86.63 271

CHOSEN 280x420		77.35 210	76.95 198	78.55 284	87.07 224	62.68 232	69.71 375	82.95 350	68.80 264	71.48 212	87.27 226	66.03 69	84.00 362	76.47 148	82.81 160	88.95 232

PS-MVSNAJss		77.26 211	76.31 205	80.13 255	80.64 313	59.16 296	90.63 224	91.06 191	72.80 180	68.58 249	84.57 255	53.55 217	93.96 233	72.97 170	71.96 252	87.27 260

gg-mvs-nofinetune		77.18 212	74.31 233	85.80 94	91.42 120	68.36 79	71.78 369	94.72 34	49.61 370	77.12 149	45.92 393	77.41 8	93.98 232	67.62 224	93.16 57	95.05 82

WB-MVSnew		77.14 213	76.18 208	80.01 259	86.18 240	63.24 216	91.26 199	94.11 60	71.72 214	73.52 184	87.29 225	45.14 292	93.00 253	56.98 297	79.42 190	83.80 313

MVP-Stereo		77.12 214	76.23 206	79.79 267	81.72 303	66.34 134	89.29 255	90.88 195	70.56 243	62.01 312	82.88 271	49.34 254	94.13 220	65.55 249	93.80 45	78.88 366
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 215	75.37 218	82.20 205	89.25 166	62.11 243	82.06 325	89.09 266	76.77 118	70.84 217	87.12 227	41.43 306	95.01 184	67.23 228	74.55 228	89.48 228

dmvs_re		76.93 216	75.36 219	81.61 220	87.78 209	60.71 272	80.00 346	87.99 303	79.42 73	69.02 240	89.47 191	46.77 275	94.32 210	63.38 263	74.45 231	89.81 221

X-MVStestdata		76.86 217	74.13 237	85.05 119	93.22 67	63.78 196	92.92 121	92.66 118	73.99 152	78.18 135	10.19 408	55.25 195	97.41 68	79.16 129	91.58 78	93.95 131

DU-MVS		76.86 217	75.84 212	79.91 263	82.96 292	60.26 279	91.26 199	91.54 166	76.46 123	68.88 243	86.35 235	56.16 186	92.13 287	66.38 238	62.55 318	87.35 257

mvsmamba		76.85 219	75.71 215	80.25 251	83.07 291	59.16 296	91.44 184	80.64 357	76.84 115	67.95 255	86.33 237	46.17 285	94.24 217	76.06 150	72.92 244	87.36 256

Anonymous20240529		76.84 220	74.15 236	84.88 124	91.02 128	64.95 168	93.84 84	91.09 187	53.57 359	73.00 187	87.42 222	35.91 339	97.32 77	69.14 210	72.41 250	92.36 176

c3_l		76.83 221	75.47 217	80.93 240	85.02 263	64.18 190	90.39 228	88.11 300	71.66 215	66.65 276	81.64 287	63.58 104	92.56 274	69.31 207	62.86 315	86.04 283

WR-MVS		76.76 222	75.74 214	79.82 266	84.60 268	62.27 241	92.60 137	92.51 125	76.06 125	67.87 260	85.34 246	56.76 178	90.24 317	62.20 273	63.69 313	86.94 265

v1144		76.73 223	74.88 223	82.27 201	80.23 319	66.60 128	91.68 180	90.21 221	73.69 162	69.06 239	81.89 282	52.73 226	94.40 209	69.21 208	65.23 296	85.80 289

IterMVS-LS		76.49 224	75.18 222	80.43 246	84.49 271	62.74 230	90.64 222	88.80 279	72.40 190	65.16 283	81.72 285	60.98 132	92.27 285	67.74 222	64.65 304	86.29 275
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 225	74.55 229	82.19 206	79.14 333	67.82 95	90.26 233	89.42 250	73.75 160	68.63 248	81.89 282	51.31 238	94.09 222	71.69 187	64.84 300	84.66 306

v148		76.19 226	74.47 231	81.36 225	80.05 321	64.44 177	91.75 178	90.23 219	73.68 163	67.13 269	80.84 302	55.92 191	93.86 239	68.95 212	61.73 329	85.76 292

Effi-MVS+-dtu		76.14 227	75.28 221	78.72 283	83.22 288	55.17 332	89.87 244	87.78 306	75.42 133	67.98 254	81.43 291	45.08 293	92.52 276	75.08 158	71.63 253	88.48 241

cl____		76.07 228	74.67 224	80.28 249	85.15 259	61.76 250	90.12 236	88.73 281	71.16 230	65.43 280	81.57 289	61.15 129	92.95 255	66.54 235	62.17 322	86.13 281

DIV-MVS_self_test		76.07 228	74.67 224	80.28 249	85.14 260	61.75 251	90.12 236	88.73 281	71.16 230	65.42 281	81.60 288	61.15 129	92.94 259	66.54 235	62.16 324	86.14 279

FMVSNet2		76.07 228	74.01 239	82.26 203	88.85 176	67.66 99	91.33 196	91.61 164	70.84 237	65.98 277	82.25 278	48.03 265	92.00 291	58.46 292	68.73 273	87.10 262

v144192		76.05 231	74.03 238	82.12 209	79.50 327	66.55 130	91.39 190	89.71 243	72.30 193	68.17 252	81.33 294	51.75 233	94.03 230	67.94 220	64.19 306	85.77 290

NR-MVSNet		76.05 231	74.59 227	80.44 245	82.96 292	62.18 242	90.83 215	91.73 156	77.12 112	60.96 315	86.35 235	59.28 153	91.80 294	60.74 280	61.34 333	87.35 257

v1192		75.98 233	73.92 240	82.15 207	79.73 323	66.24 137	91.22 202	89.75 237	72.67 182	68.49 250	81.42 292	49.86 250	94.27 214	67.08 230	65.02 298	85.95 286

FE-MVS		75.97 234	73.02 250	84.82 126	89.78 151	65.56 152	77.44 358	91.07 190	64.55 295	72.66 192	79.85 317	46.05 286	96.69 117	54.97 304	80.82 181	92.21 185

eth_miper_zixun_eth		75.96 235	74.40 232	80.66 242	84.66 267	63.02 221	89.28 256	88.27 296	71.88 206	65.73 278	81.65 286	59.45 149	92.81 263	68.13 217	60.53 338	86.14 279

TranMVSNet+NR-MVSNet		75.86 236	74.52 230	79.89 264	82.44 297	60.64 275	91.37 193	91.37 173	76.63 120	67.65 262	86.21 239	52.37 229	91.55 300	61.84 275	60.81 336	87.48 252

SCA		75.82 237	72.76 253	85.01 121	86.63 231	70.08 39	81.06 335	89.19 259	71.60 221	70.01 228	77.09 338	45.53 288	90.25 314	60.43 282	73.27 240	94.68 98

LPG-MVS_test		75.82 237	74.58 228	79.56 273	84.31 275	59.37 292	90.44 225	89.73 240	69.49 254	64.86 284	88.42 201	38.65 315	94.30 212	72.56 177	72.76 245	85.01 303

GBi-Net		75.65 239	73.83 241	81.10 233	88.85 176	65.11 163	90.01 240	90.32 211	70.84 237	67.04 270	80.25 312	48.03 265	91.54 301	59.80 287	69.34 265	86.64 268

test1		75.65 239	73.83 241	81.10 233	88.85 176	65.11 163	90.01 240	90.32 211	70.84 237	67.04 270	80.25 312	48.03 265	91.54 301	59.80 287	69.34 265	86.64 268

v1921920		75.63 241	73.49 246	82.06 213	79.38 328	66.35 133	91.07 209	89.48 246	71.98 201	67.99 253	81.22 297	49.16 259	93.90 236	66.56 234	64.56 305	85.92 288

ACMP	71.68 10	75.58 242	74.23 235	79.62 271	84.97 264	59.64 287	90.80 216	89.07 268	70.39 244	62.95 305	87.30 224	38.28 319	93.87 237	72.89 171	71.45 256	85.36 299
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 243	73.26 248	81.61 220	80.67 312	66.82 121	89.54 250	89.27 255	71.65 216	63.30 301	80.30 311	54.99 201	94.06 225	67.33 227	62.33 321	83.94 311

tpm cat1		75.30 244	72.21 262	84.58 141	88.52 182	67.77 96	78.16 356	88.02 302	61.88 322	68.45 251	76.37 344	60.65 135	94.03 230	53.77 310	74.11 234	91.93 190

PLC	68.80 14	75.23 245	73.68 244	79.86 265	92.93 76	58.68 302	90.64 222	88.30 294	60.90 327	64.43 292	90.53 169	42.38 303	94.57 201	56.52 298	76.54 220	86.33 274
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 246	72.98 251	81.88 215	79.20 330	66.00 141	90.75 218	89.11 265	71.63 220	67.41 266	81.22 297	47.36 273	93.87 237	65.46 250	64.72 303	85.77 290

Fast-Effi-MVS+-dtu		75.04 247	73.37 247	80.07 256	80.86 308	59.52 290	91.20 204	85.38 328	71.90 204	65.20 282	84.84 251	41.46 305	92.97 254	66.50 237	72.96 243	87.73 249

dp		75.01 248	72.09 263	83.76 163	89.28 165	66.22 138	79.96 348	89.75 237	71.16 230	67.80 261	77.19 337	51.81 232	92.54 275	50.39 318	71.44 257	92.51 174

TAPA-MVS	70.22 12	74.94 249	73.53 245	79.17 278	90.40 140	52.07 345	89.19 259	89.61 244	62.69 314	70.07 227	92.67 128	48.89 262	94.32 210	38.26 370	79.97 186	91.12 206
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 250	72.54 259	81.46 223	80.33 317	66.71 125	89.15 260	89.08 267	70.94 235	63.08 304	79.86 316	52.52 227	94.04 228	65.70 246	62.17 322	83.64 314

XVG-OURS-SEG-HR		74.70 251	73.08 249	79.57 272	78.25 345	57.33 317	80.49 338	87.32 309	63.22 307	68.76 246	90.12 186	44.89 294	91.59 299	70.55 197	74.09 235	89.79 222

ACMM	69.62 13	74.34 252	72.73 255	79.17 278	84.25 277	57.87 308	90.36 229	89.93 230	63.17 309	65.64 279	86.04 242	37.79 327	94.10 221	65.89 243	71.52 255	85.55 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 253	72.30 261	80.32 247	91.49 119	61.66 253	90.85 214	80.72 356	56.67 351	63.85 296	90.64 166	46.75 276	90.84 309	53.79 309	75.99 224	88.47 242

XVG-OURS		74.25 254	72.46 260	79.63 270	78.45 343	57.59 313	80.33 340	87.39 308	63.86 300	68.76 246	89.62 190	40.50 309	91.72 296	69.00 211	74.25 233	89.58 225

test_fmvs1		74.07 255	73.69 243	75.22 315	78.91 337	47.34 369	89.06 263	74.69 371	63.68 302	79.41 120	91.59 154	24.36 372	87.77 339	85.22 80	76.26 222	90.55 213

CVMVSNet		74.04 256	74.27 234	73.33 330	85.33 255	43.94 380	89.53 251	88.39 291	54.33 358	70.37 223	90.13 183	49.17 258	84.05 360	61.83 276	79.36 192	91.99 189

Baseline_NR-MVSNet		73.99 257	72.83 252	77.48 296	80.78 310	59.29 295	91.79 173	84.55 336	68.85 263	68.99 241	80.70 303	56.16 186	92.04 290	62.67 270	60.98 335	81.11 346

pmmvs4		73.92 258	71.81 267	80.25 251	79.17 331	65.24 159	87.43 286	87.26 311	67.64 275	63.46 299	83.91 262	48.96 261	91.53 304	62.94 267	65.49 292	83.96 310

D2MVS		73.80 259	72.02 264	79.15 280	79.15 332	62.97 222	88.58 269	90.07 224	72.94 175	59.22 324	78.30 326	42.31 304	92.70 269	65.59 248	72.00 251	81.79 341

CR-MVSNet		73.79 260	70.82 275	82.70 189	83.15 289	67.96 92	70.25 372	84.00 341	73.67 164	69.97 230	72.41 358	57.82 165	89.48 325	52.99 313	73.13 241	90.64 211

test_djsdf		73.76 261	72.56 258	77.39 298	77.00 354	53.93 338	89.07 261	90.69 198	65.80 287	63.92 294	82.03 281	43.14 301	92.67 270	72.83 172	68.53 274	85.57 294

pmmvs5		73.35 262	71.52 269	78.86 282	78.64 341	60.61 276	91.08 207	86.90 313	67.69 272	63.32 300	83.64 263	44.33 296	90.53 311	62.04 274	66.02 290	85.46 297

Anonymous20231211		73.08 263	70.39 279	81.13 231	90.62 136	63.33 214	91.40 188	90.06 226	51.84 364	64.46 291	80.67 305	36.49 337	94.07 224	63.83 260	64.17 307	85.98 285

tt0805		73.07 264	70.73 276	80.07 256	78.37 344	57.05 319	87.78 281	92.18 137	61.23 326	67.04 270	86.49 234	31.35 357	94.58 199	65.06 253	67.12 283	88.57 239

miper_lstm_enhance		73.05 265	71.73 268	77.03 302	83.80 281	58.32 305	81.76 326	88.88 275	69.80 252	61.01 314	78.23 328	57.19 170	87.51 343	65.34 251	59.53 343	85.27 302

jajsoiax		73.05 265	71.51 270	77.67 293	77.46 351	54.83 334	88.81 265	90.04 227	69.13 261	62.85 307	83.51 265	31.16 358	92.75 266	70.83 192	69.80 261	85.43 298

LCM-MVSNet-Re		72.93 267	71.84 266	76.18 311	88.49 183	48.02 364	80.07 345	70.17 381	73.96 155	52.25 354	80.09 315	49.98 248	88.24 333	67.35 225	84.23 150	92.28 180

pm-mvs1		72.89 268	71.09 272	78.26 288	79.10 334	57.62 312	90.80 216	89.30 254	67.66 273	62.91 306	81.78 284	49.11 260	92.95 255	60.29 284	58.89 346	84.22 309

tpmvs		72.88 269	69.76 285	82.22 204	90.98 129	67.05 116	78.22 355	88.30 294	63.10 310	64.35 293	74.98 351	55.09 200	94.27 214	43.25 350	69.57 264	85.34 300

test0.0.03 1		72.76 270	72.71 256	72.88 334	80.25 318	47.99 365	91.22 202	89.45 248	71.51 225	62.51 310	87.66 218	53.83 213	85.06 356	50.16 320	67.84 281	85.58 293

UniMVSNet_ETH3D		72.74 271	70.53 278	79.36 275	78.62 342	56.64 323	85.01 302	89.20 258	63.77 301	64.84 286	84.44 257	34.05 346	91.86 293	63.94 259	70.89 260	89.57 226

mvs_tets		72.71 272	71.11 271	77.52 294	77.41 352	54.52 336	88.45 271	89.76 236	68.76 266	62.70 308	83.26 268	29.49 362	92.71 267	70.51 198	69.62 263	85.34 300

FMVSNet1		72.71 272	69.91 283	81.10 233	83.60 285	65.11 163	90.01 240	90.32 211	63.92 299	63.56 298	80.25 312	36.35 338	91.54 301	54.46 306	66.75 286	86.64 268

test_fmvs1_n		72.69 274	71.92 265	74.99 318	71.15 373	47.08 371	87.34 288	75.67 366	63.48 304	78.08 137	91.17 161	20.16 383	87.87 336	84.65 88	75.57 226	90.01 219

IterMVS		72.65 275	70.83 273	78.09 290	82.17 299	62.96 223	87.64 284	86.28 319	71.56 223	60.44 317	78.85 324	45.42 290	86.66 347	63.30 265	61.83 326	84.65 307
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 276	72.74 254	72.10 342	87.87 205	49.45 359	88.07 275	89.01 270	72.91 177	63.11 302	88.10 210	63.63 99	85.54 352	32.73 384	69.23 268	81.32 344

PatchMatch-RL		72.06 277	69.98 280	78.28 287	89.51 159	55.70 329	83.49 311	83.39 348	61.24 325	63.72 297	82.76 272	34.77 343	93.03 252	53.37 312	77.59 207	86.12 282

PVSNet_0	68.08 15	71.81 278	68.32 294	82.27 201	84.68 266	62.31 240	88.68 267	90.31 214	75.84 127	57.93 335	80.65 306	37.85 326	94.19 218	69.94 200	29.05 398	90.31 215

MIMVSNet		71.64 279	68.44 292	81.23 228	81.97 302	64.44 177	73.05 368	88.80 279	69.67 253	64.59 287	74.79 352	32.79 349	87.82 337	53.99 308	76.35 221	91.42 196

test_vis1_n		71.63 280	70.73 276	74.31 325	69.63 379	47.29 370	86.91 292	72.11 376	63.21 308	75.18 168	90.17 181	20.40 381	85.76 351	84.59 89	74.42 232	89.87 220

IterMVS-SCA-FT		71.55 281	69.97 281	76.32 309	81.48 304	60.67 274	87.64 284	85.99 324	66.17 285	59.50 322	78.88 323	45.53 288	83.65 364	62.58 271	61.93 325	84.63 308

v7n		71.31 282	68.65 289	79.28 276	76.40 356	60.77 268	86.71 295	89.45 248	64.17 298	58.77 329	78.24 327	44.59 295	93.54 243	57.76 294	61.75 328	83.52 317

anonymousdsp		71.14 283	69.37 287	76.45 308	72.95 368	54.71 335	84.19 306	88.88 275	61.92 321	62.15 311	79.77 318	38.14 322	91.44 306	68.90 213	67.45 282	83.21 323

F-COLMAP		70.66 284	68.44 292	77.32 299	86.37 237	55.91 327	88.00 277	86.32 318	56.94 349	57.28 338	88.07 212	33.58 347	92.49 277	51.02 316	68.37 275	83.55 315

WR-MVS_H		70.59 285	69.94 282	72.53 336	81.03 307	51.43 348	87.35 287	92.03 141	67.38 276	60.23 319	80.70 303	55.84 192	83.45 366	46.33 340	58.58 348	82.72 330

CP-MVSNet		70.50 286	69.91 283	72.26 339	80.71 311	51.00 351	87.23 289	90.30 215	67.84 271	59.64 321	82.69 273	50.23 247	82.30 374	51.28 315	59.28 344	83.46 319

RPMNet		70.42 287	65.68 306	84.63 139	83.15 289	67.96 92	70.25 372	90.45 205	46.83 378	69.97 230	65.10 377	56.48 185	95.30 177	35.79 375	73.13 241	90.64 211

testing3		70.38 288	70.83 273	69.03 353	85.82 249	43.93 381	90.72 219	90.56 204	68.06 270	60.24 318	86.82 231	64.83 83	84.12 358	26.33 391	64.10 308	79.04 365

tfpnnormal		70.10 289	67.36 297	78.32 286	83.45 287	60.97 264	88.85 264	92.77 111	64.85 294	60.83 316	78.53 325	43.52 299	93.48 245	31.73 387	61.70 330	80.52 353

TransMVSNet (Re)		70.07 290	67.66 296	77.31 300	80.62 314	59.13 298	91.78 175	84.94 333	65.97 286	60.08 320	80.44 308	50.78 241	91.87 292	48.84 326	45.46 376	80.94 348

CL-MVSNet_self_test		69.92 291	68.09 295	75.41 314	73.25 367	55.90 328	90.05 239	89.90 231	69.96 249	61.96 313	76.54 341	51.05 240	87.64 340	49.51 324	50.59 368	82.70 332

DP-MVS		69.90 292	66.48 299	80.14 254	95.36 28	62.93 224	89.56 248	76.11 364	50.27 369	57.69 336	85.23 247	39.68 311	95.73 154	33.35 380	71.05 259	81.78 342

PS-CasMVS		69.86 293	69.13 288	72.07 343	80.35 316	50.57 353	87.02 291	89.75 237	67.27 277	59.19 325	82.28 277	46.58 278	82.24 375	50.69 317	59.02 345	83.39 321

Syy-MVS		69.65 294	69.52 286	70.03 349	87.87 205	43.21 382	88.07 275	89.01 270	72.91 177	63.11 302	88.10 210	45.28 291	85.54 352	22.07 395	69.23 268	81.32 344

MSDG		69.54 295	65.73 305	80.96 238	85.11 262	63.71 201	84.19 306	83.28 349	56.95 348	54.50 345	84.03 259	31.50 355	96.03 144	42.87 354	69.13 270	83.14 325

PEN-MVS		69.46 296	68.56 290	72.17 341	79.27 329	49.71 357	86.90 293	89.24 256	67.24 280	59.08 326	82.51 276	47.23 274	83.54 365	48.42 328	57.12 349	83.25 322

LS3D		69.17 297	66.40 301	77.50 295	91.92 105	56.12 326	85.12 301	80.37 358	46.96 376	56.50 340	87.51 221	37.25 330	93.71 240	32.52 386	79.40 191	82.68 333

PatchT		69.11 298	65.37 310	80.32 247	82.07 301	63.68 204	67.96 381	87.62 307	50.86 367	69.37 234	65.18 376	57.09 171	88.53 331	41.59 359	66.60 287	88.74 236

KD-MVS_2432*1600		69.03 299	66.37 302	77.01 303	85.56 253	61.06 262	81.44 331	90.25 217	67.27 277	58.00 333	76.53 342	54.49 205	87.63 341	48.04 330	35.77 390	82.34 336

miper_refine_blended		69.03 299	66.37 302	77.01 303	85.56 253	61.06 262	81.44 331	90.25 217	67.27 277	58.00 333	76.53 342	54.49 205	87.63 341	48.04 330	35.77 390	82.34 336

mvsany_test1		68.77 301	68.56 290	69.39 351	73.57 366	45.88 376	80.93 336	60.88 394	59.65 336	71.56 211	90.26 179	43.22 300	75.05 384	74.26 166	62.70 317	87.25 261

ACMH	63.93 17	68.62 302	64.81 312	80.03 258	85.22 258	63.25 215	87.72 282	84.66 335	60.83 328	51.57 357	79.43 322	27.29 368	94.96 186	41.76 357	64.84 300	81.88 340
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 303	65.41 309	77.96 291	78.69 340	62.93 224	89.86 245	89.17 260	60.55 329	50.27 362	77.73 332	22.60 377	94.06 225	47.18 336	72.65 247	76.88 374

ADS-MVSNet		68.54 304	64.38 319	81.03 237	88.06 199	66.90 120	68.01 379	84.02 340	57.57 343	64.48 289	69.87 368	38.68 313	89.21 327	40.87 361	67.89 279	86.97 263

DTE-MVSNet		68.46 305	67.33 298	71.87 345	77.94 349	49.00 362	86.16 298	88.58 288	66.36 284	58.19 330	82.21 279	46.36 279	83.87 363	44.97 347	55.17 356	82.73 329

our_test_3		68.29 306	64.69 314	79.11 281	78.92 335	64.85 170	88.40 272	85.06 331	60.32 332	52.68 352	76.12 346	40.81 308	89.80 324	44.25 349	55.65 354	82.67 334

Patchmatch-RL test		68.17 307	64.49 317	79.19 277	71.22 372	53.93 338	70.07 374	71.54 380	69.22 258	56.79 339	62.89 380	56.58 183	88.61 328	69.53 204	52.61 363	95.03 84

XVG-ACMP-BASELINE		68.04 308	65.53 308	75.56 313	74.06 365	52.37 343	78.43 352	85.88 325	62.03 319	58.91 328	81.21 299	20.38 382	91.15 308	60.69 281	68.18 276	83.16 324

FMVSNet5		68.04 308	65.66 307	75.18 317	84.43 273	57.89 307	83.54 310	86.26 320	61.83 323	53.64 350	73.30 355	37.15 333	85.08 355	48.99 325	61.77 327	82.56 335

ppachtmachnet_test		67.72 310	63.70 321	79.77 268	78.92 335	66.04 140	88.68 267	82.90 351	60.11 334	55.45 342	75.96 347	39.19 312	90.55 310	39.53 365	52.55 364	82.71 331

ACMH+	65.35 16	67.65 311	64.55 315	76.96 305	84.59 269	57.10 318	88.08 274	80.79 355	58.59 342	53.00 351	81.09 301	26.63 370	92.95 255	46.51 338	61.69 331	80.82 349

pmmvs6		67.57 312	64.76 313	76.00 312	72.82 370	53.37 340	88.71 266	86.78 317	53.19 360	57.58 337	78.03 330	35.33 342	92.41 279	55.56 302	54.88 358	82.21 338

Anonymous20231206		67.53 313	65.78 304	72.79 335	74.95 361	47.59 367	88.23 273	87.32 309	61.75 324	58.07 332	77.29 335	37.79 327	87.29 345	42.91 352	63.71 312	83.48 318

Patchmtry		67.53 313	63.93 320	78.34 285	82.12 300	64.38 181	68.72 376	84.00 341	48.23 375	59.24 323	72.41 358	57.82 165	89.27 326	46.10 341	56.68 353	81.36 343

USDC		67.43 315	64.51 316	76.19 310	77.94 349	55.29 331	78.38 353	85.00 332	73.17 170	48.36 369	80.37 309	21.23 379	92.48 278	52.15 314	64.02 310	80.81 350

ADS-MVSNet2		66.90 316	63.44 323	77.26 301	88.06 199	60.70 273	68.01 379	75.56 368	57.57 343	64.48 289	69.87 368	38.68 313	84.10 359	40.87 361	67.89 279	86.97 263

CMPMVS	48.56 21	66.77 317	64.41 318	73.84 327	70.65 376	50.31 354	77.79 357	85.73 327	45.54 380	44.76 379	82.14 280	35.40 341	90.14 320	63.18 266	74.54 230	81.07 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 318	62.92 326	76.80 307	76.51 355	57.77 309	89.22 257	83.41 347	55.48 355	53.86 349	77.84 331	26.28 371	93.95 234	34.90 377	68.76 272	78.68 368

LTVRE_ROB	59.60 19	66.27 319	63.54 322	74.45 322	84.00 280	51.55 347	67.08 382	83.53 345	58.78 340	54.94 344	80.31 310	34.54 344	93.23 249	40.64 363	68.03 277	78.58 369
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		66.06 320	62.45 329	76.88 306	81.42 306	54.45 337	57.49 394	88.67 284	49.36 371	63.86 295	46.86 392	56.06 189	90.25 314	49.53 323	68.83 271	85.95 286

Patchmatch-test		65.86 321	60.94 335	80.62 244	83.75 282	58.83 300	58.91 393	75.26 370	44.50 383	50.95 361	77.09 338	58.81 157	87.90 335	35.13 376	64.03 309	95.12 80

UnsupCasMVSNet_eth		65.79 322	63.10 324	73.88 326	70.71 375	50.29 355	81.09 334	89.88 232	72.58 184	49.25 367	74.77 353	32.57 351	87.43 344	55.96 301	41.04 383	83.90 312

test_fmvs2		65.78 323	64.84 311	68.60 355	66.54 384	41.71 384	83.27 315	69.81 382	54.38 357	67.91 257	84.54 256	15.35 388	81.22 379	75.65 153	66.16 289	82.88 326

dmvs_testset		65.55 324	66.45 300	62.86 365	79.87 322	22.35 408	76.55 360	71.74 378	77.42 111	55.85 341	87.77 217	51.39 237	80.69 380	31.51 390	65.92 291	85.55 295

pmmvs-eth3d		65.53 325	62.32 330	75.19 316	69.39 380	59.59 288	82.80 322	83.43 346	62.52 315	51.30 359	72.49 356	32.86 348	87.16 346	55.32 303	50.73 367	78.83 367

SixPastTwentyTwo		64.92 326	61.78 333	74.34 324	78.74 339	49.76 356	83.42 314	79.51 361	62.86 311	50.27 362	77.35 333	30.92 360	90.49 312	45.89 342	47.06 373	82.78 327

OurMVSNet-221017-0		64.68 327	62.17 331	72.21 340	76.08 359	47.35 368	80.67 337	81.02 354	56.19 352	51.60 356	79.66 320	27.05 369	88.56 330	53.60 311	53.63 361	80.71 351

test_0402		64.54 328	61.09 334	74.92 319	84.10 279	60.75 270	87.95 278	79.71 360	52.03 362	52.41 353	77.20 336	32.21 353	91.64 297	23.14 393	61.03 334	72.36 382

testgi		64.48 329	62.87 327	69.31 352	71.24 371	40.62 387	85.49 299	79.92 359	65.36 291	54.18 347	83.49 266	23.74 375	84.55 357	41.60 358	60.79 337	82.77 328

RPSCF		64.24 330	61.98 332	71.01 347	76.10 358	45.00 377	75.83 364	75.94 365	46.94 377	58.96 327	84.59 254	31.40 356	82.00 376	47.76 334	60.33 342	86.04 283

EU-MVSNet		64.01 331	63.01 325	67.02 361	74.40 364	38.86 392	83.27 315	86.19 322	45.11 381	54.27 346	81.15 300	36.91 336	80.01 382	48.79 327	57.02 350	82.19 339

test20.03		63.83 332	62.65 328	67.38 360	70.58 377	39.94 388	86.57 296	84.17 338	63.29 306	51.86 355	77.30 334	37.09 334	82.47 372	38.87 369	54.13 360	79.73 359

MDA-MVSNet_test_wron		63.78 333	60.16 336	74.64 320	78.15 347	60.41 277	83.49 311	84.03 339	56.17 354	39.17 388	71.59 364	37.22 331	83.24 369	42.87 354	48.73 370	80.26 356

YYNet1		63.76 334	60.14 337	74.62 321	78.06 348	60.19 282	83.46 313	83.99 343	56.18 353	39.25 387	71.56 365	37.18 332	83.34 367	42.90 353	48.70 371	80.32 355

K. test v3		63.09 335	59.61 339	73.53 329	76.26 357	49.38 361	83.27 315	77.15 363	64.35 297	47.77 371	72.32 360	28.73 364	87.79 338	49.93 322	36.69 389	83.41 320

COLMAP_ROB	57.96 20	62.98 336	59.65 338	72.98 333	81.44 305	53.00 342	83.75 309	75.53 369	48.34 374	48.81 368	81.40 293	24.14 373	90.30 313	32.95 382	60.52 339	75.65 377
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	59.08 340	71.10 346	67.19 383	48.72 363	83.91 308	85.23 330	50.38 368	47.84 370	71.22 367	20.74 380	85.51 354	46.47 339	58.75 347	79.06 364

AllTest		61.66 338	58.06 342	72.46 337	79.57 324	51.42 349	80.17 343	68.61 384	51.25 365	45.88 373	81.23 295	19.86 384	86.58 348	38.98 367	57.01 351	79.39 361

UnsupCasMVSNet_bld		61.60 339	57.71 343	73.29 331	68.73 381	51.64 346	78.61 351	89.05 269	57.20 347	46.11 372	61.96 383	28.70 365	88.60 329	50.08 321	38.90 387	79.63 360

MDA-MVSNet-bldmvs		61.54 340	57.70 344	73.05 332	79.53 326	57.00 322	83.08 319	81.23 353	57.57 343	34.91 391	72.45 357	32.79 349	86.26 350	35.81 374	41.95 381	75.89 376

KD-MVS_self_test		60.87 341	58.60 341	67.68 358	66.13 385	39.93 389	75.63 365	84.70 334	57.32 346	49.57 365	68.45 371	29.55 361	82.87 370	48.09 329	47.94 372	80.25 357

TinyColmap		60.32 342	56.42 349	72.00 344	78.78 338	53.18 341	78.36 354	75.64 367	52.30 361	41.59 386	75.82 349	14.76 391	88.35 332	35.84 373	54.71 359	74.46 378

MVS-HIRNet		60.25 343	55.55 350	74.35 323	84.37 274	56.57 324	71.64 370	74.11 372	34.44 391	45.54 377	42.24 398	31.11 359	89.81 322	40.36 364	76.10 223	76.67 375

MIMVSNet1		60.16 344	57.33 345	68.67 354	69.71 378	44.13 379	78.92 350	84.21 337	55.05 356	44.63 380	71.85 362	23.91 374	81.54 378	32.63 385	55.03 357	80.35 354

PM-MVS		59.40 345	56.59 347	67.84 356	63.63 387	41.86 383	76.76 359	63.22 391	59.01 339	51.07 360	72.27 361	11.72 394	83.25 368	61.34 277	50.28 369	78.39 370

new-patchmatchnet		59.30 346	56.48 348	67.79 357	65.86 386	44.19 378	82.47 323	81.77 352	59.94 335	43.65 383	66.20 375	27.67 367	81.68 377	39.34 366	41.40 382	77.50 373

test_vis1_rt		59.09 347	57.31 346	64.43 363	68.44 382	46.02 375	83.05 320	48.63 403	51.96 363	49.57 365	63.86 379	16.30 386	80.20 381	71.21 190	62.79 316	67.07 388

test_fmvs3		56.82 348	54.86 351	62.69 366	53.59 397	35.47 394	75.87 363	65.64 389	43.91 384	55.10 343	71.43 366	6.91 402	74.40 387	68.64 215	52.63 362	78.20 371

DSMNet-mixed		56.78 349	54.44 352	63.79 364	63.21 388	29.44 403	64.43 385	64.10 390	42.12 388	51.32 358	71.60 363	31.76 354	75.04 385	36.23 372	65.20 297	86.87 266

pmmvs3		55.51 350	51.50 355	67.53 359	57.90 395	50.93 352	80.37 339	73.66 373	40.63 389	44.15 382	64.75 378	16.30 386	78.97 383	44.77 348	40.98 385	72.69 380

TDRefinement		55.28 351	51.58 354	66.39 362	59.53 394	46.15 374	76.23 362	72.80 374	44.60 382	42.49 384	76.28 345	15.29 389	82.39 373	33.20 381	43.75 378	70.62 384

LF4IMVS		54.01 352	52.12 353	59.69 367	62.41 390	39.91 390	68.59 377	68.28 386	42.96 387	44.55 381	75.18 350	14.09 393	68.39 393	41.36 360	51.68 365	70.78 383

N_pmnet		50.55 353	49.11 356	54.88 373	77.17 353	4.02 416	84.36 305	2.00 414	48.59 372	45.86 375	68.82 370	32.22 352	82.80 371	31.58 388	51.38 366	77.81 372

new_pmnet		49.31 354	46.44 357	57.93 368	62.84 389	40.74 386	68.47 378	62.96 392	36.48 390	35.09 390	57.81 387	14.97 390	72.18 389	32.86 383	46.44 374	60.88 390

mvsany_test3		48.86 355	46.35 358	56.41 369	46.00 403	31.67 399	62.26 387	47.25 404	43.71 385	45.54 377	68.15 372	10.84 395	64.44 401	57.95 293	35.44 392	73.13 379

test_f		46.58 356	43.45 360	55.96 370	45.18 404	32.05 398	61.18 388	49.49 402	33.39 392	42.05 385	62.48 382	7.00 401	65.56 397	47.08 337	43.21 380	70.27 385

WB-MVS		46.23 357	44.94 359	50.11 377	62.13 391	21.23 410	76.48 361	55.49 396	45.89 379	35.78 389	61.44 385	35.54 340	72.83 388	9.96 404	21.75 399	56.27 392

FPMVS		45.64 358	43.10 362	53.23 375	51.42 400	36.46 393	64.97 384	71.91 377	29.13 395	27.53 395	61.55 384	9.83 397	65.01 399	16.00 401	55.58 355	58.22 391

SSC-MVS		44.51 359	43.35 361	47.99 381	61.01 393	18.90 412	74.12 367	54.36 397	43.42 386	34.10 392	60.02 386	34.42 345	70.39 391	9.14 406	19.57 400	54.68 393

EGC-MVSNET		42.35 360	38.09 363	55.11 372	74.57 362	46.62 373	71.63 371	55.77 395	0.04 409	0.24 410	62.70 381	14.24 392	74.91 386	17.59 398	46.06 375	43.80 395

LCM-MVSNet		40.54 361	35.79 366	54.76 374	36.92 410	30.81 400	51.41 397	69.02 383	22.07 397	24.63 397	45.37 394	4.56 406	65.81 396	33.67 379	34.50 393	67.67 386

APD_test1		40.50 362	37.31 365	50.09 378	51.88 398	35.27 395	59.45 392	52.59 399	21.64 398	26.12 396	57.80 388	4.56 406	66.56 395	22.64 394	39.09 386	48.43 394

test_vis3_rt		40.46 363	37.79 364	48.47 380	44.49 405	33.35 397	66.56 383	32.84 411	32.39 393	29.65 393	39.13 401	3.91 409	68.65 392	50.17 319	40.99 384	43.40 396

ANet_high		40.27 364	35.20 367	55.47 371	34.74 411	34.47 396	63.84 386	71.56 379	48.42 373	18.80 400	41.08 399	9.52 398	64.45 400	20.18 396	8.66 407	67.49 387

test_method		38.59 365	35.16 368	48.89 379	54.33 396	21.35 409	45.32 400	53.71 398	7.41 406	28.74 394	51.62 390	8.70 399	52.87 404	33.73 378	32.89 394	72.47 381

PMMVS2		37.93 366	33.61 369	50.92 376	46.31 402	24.76 406	60.55 391	50.05 400	28.94 396	20.93 398	47.59 391	4.41 408	65.13 398	25.14 392	18.55 402	62.87 389

Gipuma		34.91 367	31.44 370	45.30 382	70.99 374	39.64 391	19.85 404	72.56 375	20.10 400	16.16 404	21.47 405	5.08 405	71.16 390	13.07 402	43.70 379	25.08 402
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 368	29.47 371	42.67 384	41.89 407	30.81 400	52.07 395	43.45 405	15.45 401	18.52 401	44.82 395	2.12 410	58.38 402	16.05 399	30.87 396	38.83 397

APD_test2		32.77 368	29.47 371	42.67 384	41.89 407	30.81 400	52.07 395	43.45 405	15.45 401	18.52 401	44.82 395	2.12 410	58.38 402	16.05 399	30.87 396	38.83 397

PMVS	26.43 22	31.84 370	28.16 373	42.89 383	25.87 413	27.58 404	50.92 398	49.78 401	21.37 399	14.17 405	40.81 400	2.01 412	66.62 394	9.61 405	38.88 388	34.49 401
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 371	24.00 375	26.45 388	43.74 406	18.44 413	60.86 389	39.66 407	15.11 403	9.53 407	22.10 404	6.52 403	46.94 406	8.31 407	10.14 404	13.98 404

MVE	24.84 23	24.35 372	19.77 378	38.09 386	34.56 412	26.92 405	26.57 402	38.87 409	11.73 405	11.37 406	27.44 402	1.37 413	50.42 405	11.41 403	14.60 403	36.93 399
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 373	23.20 377	25.46 389	41.52 409	16.90 414	60.56 390	38.79 410	14.62 404	8.99 408	20.24 407	7.35 400	45.82 407	7.25 408	9.46 405	13.64 405

tmp_tt		22.26 374	23.75 376	17.80 390	5.23 414	12.06 415	35.26 401	39.48 408	2.82 408	18.94 399	44.20 397	22.23 378	24.64 409	36.30 371	9.31 406	16.69 403

cdsmvs_eth3d_5k		19.86 375	26.47 374	0.00 394	0.00 417	0.00 419	0.00 405	93.45 84	0.00 412	0.00 413	95.27 58	49.56 252	0.00 413	0.00 412	0.00 410	0.00 409

wuyk23d		11.30 376	10.95 379	12.33 391	48.05 401	19.89 411	25.89 403	1.92 415	3.58 407	3.12 409	1.37 409	0.64 414	15.77 410	6.23 409	7.77 408	1.35 406

ab-mvs-re		7.91 377	10.55 380	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	94.95 68	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

testmvs		7.23 378	9.62 381	0.06 393	0.04 415	0.02 418	84.98 303	0.02 416	0.03 410	0.18 411	1.21 410	0.01 416	0.02 411	0.14 410	0.01 409	0.13 408

test123		6.92 379	9.21 382	0.08 392	0.03 416	0.05 417	81.65 329	0.01 417	0.02 411	0.14 412	0.85 411	0.03 415	0.02 411	0.12 411	0.00 410	0.16 407

pcd_1.5k_mvsjas		4.46 380	5.95 383	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	53.55 217	0.00 413	0.00 412	0.00 410	0.00 409

test_blank		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

uanet_test		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

DCPMVS		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

sosnet-low-res		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

sosnet		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

uncertanet		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

Regformer		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

uanet		0.00 381	0.00 384	0.00 394	0.00 417	0.00 419	0.00 405	0.00 418	0.00 412	0.00 413	0.00 412	0.00 417	0.00 413	0.00 412	0.00 410	0.00 409

WAC-MVS						49.45 359								31.56 389

FOURS1					93.95 48	61.77 249	93.96 74	91.92 145	62.14 318	86.57 50

MSC_two_6792asdad				89.60 9	97.31 4	73.22 13		95.05 26					99.07 13	92.01 24	94.77 26	96.51 26

PC_three_1452									80.91 48	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 9	97.31 4	73.22 13		95.05 26					99.07 13	92.01 24	94.77 26	96.51 26

test_one_0601					96.32 18	69.74 50		94.18 57	71.42 227	90.67 20	96.85 16	74.45 18

eth-test2					0.00 417

eth-test					0.00 417

ZD-MVS					96.63 9	65.50 155		93.50 82	70.74 241	85.26 65	95.19 64	64.92 82	97.29 79	87.51 60	93.01 58

RE-MVS-def			80.48 142		92.02 99	58.56 303	90.90 211	90.45 205	62.76 312	78.89 126	94.46 82	49.30 255		78.77 135	86.77 127	92.28 180

IU-MVS					96.46 11	69.91 44		95.18 20	80.75 51	95.28 1				92.34 21	95.36 13	96.47 30

OPU-MVS				89.97 4	97.52 3	73.15 15	96.89 7				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

test_241102_TWO								94.41 48	71.65 216	92.07 8	97.21 4	74.58 17	99.11 6	92.34 21	95.36 13	96.59 21

test_241102_ONE					96.45 12	69.38 55		94.44 46	71.65 216	92.11 6	97.05 7	76.79 9	99.11 6

9.14			87.63 31		93.86 51		94.41 57	94.18 57	72.76 181	86.21 52	96.51 25	66.64 63	97.88 44	90.08 39	94.04 41

save fliter					93.84 52	67.89 94	95.05 42	92.66 118	78.19 94

test_0728_THIRD									72.48 186	90.55 21	96.93 11	76.24 11	99.08 11	91.53 29	94.99 17	96.43 32

test_0728_SECOND				88.70 19	96.45 12	70.43 35	96.64 11	94.37 52					99.15 2	91.91 27	94.90 22	96.51 26

test0726					96.40 15	69.99 40	96.76 9	94.33 54	71.92 202	91.89 10	97.11 6	73.77 21

GSMVS																94.68 98

test_part2					96.29 19	68.16 88				90.78 17

sam_mvs1												57.85 164				94.68 98

sam_mvs												54.91 202

ambc				69.61 350	61.38 392	41.35 385	49.07 399	85.86 326		50.18 364	66.40 374	10.16 396	88.14 334	45.73 343	44.20 377	79.32 363

MTGPA								92.23 131

test_post1							78.95 349				20.70 406	53.05 222	91.50 305	60.43 282

test_post											23.01 403	56.49 184	92.67 270

patchmatchnet-post											67.62 373	57.62 167	90.25 314

GG-mvs-BLEND				86.53 73	91.91 106	69.67 53	75.02 366	94.75 33		78.67 133	90.85 165	77.91 7	94.56 203	72.25 180	93.74 47	95.36 65

MTMP							93.77 88	32.52 412

gm-plane-assit					88.42 187	67.04 117			78.62 91		91.83 148		97.37 72	76.57 147

test9_res														89.41 43	94.96 19	95.29 70

TEST9					94.18 41	67.28 109	94.16 63	93.51 80	71.75 213	85.52 60	95.33 53	68.01 53	97.27 84

test_8					94.19 40	67.19 111	94.15 65	93.42 86	71.87 207	85.38 63	95.35 52	68.19 51	96.95 106

agg_prior2														86.41 72	94.75 30	95.33 66

agg_prior					94.16 43	66.97 119		93.31 89		84.49 71			96.75 116

TestCases				72.46 337	79.57 324	51.42 349		68.61 384	51.25 365	45.88 373	81.23 295	19.86 384	86.58 348	38.98 367	57.01 351	79.39 361

test_prior4						67.18 113	93.92 77

test_prior2							95.10 40		75.40 134	85.25 66	95.61 47	67.94 54		87.47 61	94.77 26

test_prior				86.42 76	94.71 35	67.35 108		93.10 101					96.84 113			95.05 82

旧先验2							92.00 164		59.37 338	87.54 43			93.47 246	75.39 155

新几何2							91.41 186

新几何1				84.73 132	92.32 91	64.28 186		91.46 171	59.56 337	79.77 115	92.90 122	56.95 177	96.57 121	63.40 262	92.91 60	93.34 148

旧先验1					91.94 103	60.74 271		91.50 169			94.36 86	65.23 77			91.84 73	94.55 105

无先验							92.71 129	92.61 122	62.03 319				97.01 97	66.63 233		93.97 130

原ACMM2							92.01 161

原ACMM1				84.42 146	93.21 69	64.27 187		93.40 88	65.39 290	79.51 118	92.50 130	58.11 163	96.69 117	65.27 252	93.96 42	92.32 178

test222					89.77 152	61.60 254	89.55 249	89.42 250	56.83 350	77.28 147	92.43 134	52.76 225			91.14 87	93.09 156

testdata2													96.09 138	61.26 278

segment_acmp												65.94 70

testdata				81.34 226	89.02 173	57.72 310		89.84 233	58.65 341	85.32 64	94.09 98	57.03 172	93.28 248	69.34 206	90.56 93	93.03 159

testdata1							89.21 258		77.55 107

test12				87.09 53	94.60 36	68.86 67		92.91 107		82.67 87		65.44 75	97.55 62		93.69 50	94.84 91

plane_prior7					86.94 227	61.51 255

plane_prior6					87.23 219	62.32 239						50.66 242

plane_prior5								91.31 175					95.55 167	76.74 145	78.53 201	88.39 243

plane_prior4											89.14 196

plane_prior3						61.95 247			79.09 82	72.53 196

plane_prior2							93.13 113		78.81 88

plane_prior1					87.15 221

plane_prior						62.42 235	93.85 81		79.38 74						78.80 198

n2								0.00 418

nn								0.00 418

door-mid								66.01 388

lessismore_v0				73.72 328	72.93 369	47.83 366		61.72 393		45.86 375	73.76 354	28.63 366	89.81 322	47.75 335	31.37 395	83.53 316

LGP-MVS_train				79.56 273	84.31 275	59.37 292		89.73 240	69.49 254	64.86 284	88.42 201	38.65 315	94.30 212	72.56 177	72.76 245	85.01 303

test11								93.01 103

door								66.57 387

HQP5-MVS						63.66 205

HQP-NCC					87.54 212		94.06 67		79.80 65	74.18 176

ACMP_Plane					87.54 212		94.06 67		79.80 65	74.18 176

BP-MVS														77.63 142

HQP4-MVS										74.18 176			95.61 162			88.63 237

HQP3-MVS								91.70 161							78.90 196

HQP2-MVS												51.63 235

NP-MVS					87.41 215	63.04 220					90.30 176

MDTV_nov1_ep13_2view						59.90 285	80.13 344		67.65 274	72.79 191		54.33 210		59.83 286		92.58 171

MDTV_nov1_ep13			72.61 257		89.06 172	68.48 76	80.33 340	90.11 223	71.84 209	71.81 207	75.92 348	53.01 223	93.92 235	48.04 330	73.38 239

ACMMP++_ref															71.63 253

ACMMP++															69.72 262

Test By Simon												54.21 211

ITE_SJBPF				70.43 348	74.44 363	47.06 372		77.32 362	60.16 333	54.04 348	83.53 264	23.30 376	84.01 361	43.07 351	61.58 332	80.21 358

DeepMVS_CX				34.71 387	51.45 399	24.73 407		28.48 413	31.46 394	17.49 403	52.75 389	5.80 404	42.60 408	18.18 397	19.42 401	36.81 400

High-resolution multi-view benchmark