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 10	89.33 1	85.77 54	96.26 30	72.84 28	99.38 1	92.64 20	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 97	71.10 27	97.02 3	96.04 6	88.70 2	91.57 14	96.19 32	70.12 43	98.91 18	96.83 1	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 156	76.68 2	97.29 1	95.35 15	82.87 21	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 16	97.31 4	69.91 42	93.96 70	94.37 52	72.48 186	92.07 9	96.85 16	83.82 2	99.15 2	91.53 30	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 89	92.83 78	64.03 193	93.06 112	94.33 54	82.19 28	93.65 3	96.15 34	85.89 1	97.19 82	91.02 34	97.75 1	96.43 31
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
MVS_0304		90.32 6	90.90 7	88.55 23	94.05 45	70.23 36	97.00 5	93.73 73	87.30 4	92.15 6	96.15 34	66.38 65	98.94 17	96.71 2	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 30	84.83 11	89.07 31	96.80 19	70.86 39	99.06 15	92.64 20	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 70	73.88 9	97.01 4	94.40 50	88.32 3	85.71 55	94.91 70	74.11 20	98.91 18	87.26 62	95.94 8	97.03 12
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
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 55	96.89 6	94.44 46	71.65 216	92.11 7	97.21 4	76.79 9	99.11 6	92.34 22	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 133	93.00 75	58.16 307	96.72 9	94.41 48	86.50 8	90.25 22	97.83 1	75.46 14	98.67 25	92.78 19	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 92	96.04 24	63.70 203	95.04 40	95.19 19	86.74 7	91.53 15	95.15 63	73.86 21	97.58 59	93.38 14	92.00 69	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 39	69.71 50	96.53 13	93.78 66	86.89 6	89.68 28	95.78 40	65.94 69	99.10 9	92.99 17	93.91 42	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 38	96.64 10	94.52 42	71.92 202	90.55 20	96.93 11	73.77 22	99.08 11	91.91 28	94.90 22	96.29 35
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 34	95.10 30	68.23 86	95.24 33	94.49 44	82.43 25	88.90 32	96.35 27	71.89 36	98.63 26	88.76 48	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 17	89.81 6	93.66 54	75.15 5	90.61 221	93.43 87	84.06 14	86.20 49	90.17 178	72.42 31	96.98 98	93.09 16	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 63	96.38 15	94.64 39	84.42 12	86.74 45	96.20 31	66.56 64	98.76 24	89.03 47	94.56 34	95.92 46

DPE-MVS		88.77 17	89.21 16	87.45 43	96.26 20	67.56 102	94.17 58	94.15 59	68.77 265	90.74 18	97.27 2	76.09 12	98.49 29	90.58 38	94.91 21	96.30 34
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 33	93.21 67	68.72 72	93.85 77	94.03 62	74.18 149	91.74 12	96.67 21	65.61 73	98.42 33	89.24 44	96.08 7	95.88 47
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 7	92.06 98	76.72 1	95.75 20	93.26 92	83.86 15	89.55 29	96.06 36	53.55 219	97.89 43	91.10 32	93.31 53	94.54 108

TSAR-MVS + MP.		88.11 20	88.64 18	86.54 72	91.73 111	68.04 90	90.36 226	93.55 80	82.89 20	91.29 16	92.89 121	72.27 33	96.03 144	87.99 52	94.77 26	95.54 57
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 65	93.51 61	65.32 158	95.15 36	93.84 65	78.17 94	85.93 53	94.80 73	75.80 13	98.21 34	89.38 41	88.78 104	96.59 19

DeepC-MVS_fast	79.48 2	87.95 22	88.00 25	87.79 31	95.86 27	68.32 80	95.74 21	94.11 60	83.82 16	83.49 76	96.19 32	64.53 87	98.44 31	83.42 99	94.88 25	96.61 18
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 12	91.41 123	76.43 3	95.74 21	93.12 100	83.53 18	89.55 29	95.95 38	53.45 223	97.68 50	91.07 33	92.62 60	94.54 108

EPNet		87.84 24	88.38 20	86.23 82	93.30 64	66.05 140	95.26 32	94.84 29	87.09 5	88.06 34	94.53 79	66.79 61	97.34 73	83.89 95	91.68 74	95.29 70
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 38	89.24 171	71.18 24	96.57 12	92.90 109	82.70 23	87.13 40	95.27 56	64.99 78	95.80 149	89.34 42	91.80 72	95.93 45

test_fmvsm_n_1920		87.69 26	88.50 19	85.27 113	87.05 227	63.55 210	93.69 87	91.08 190	84.18 13	90.17 24	97.04 8	67.58 56	97.99 39	95.72 5	90.03 95	94.26 116

APDe-MVS		87.54 27	87.84 26	86.65 66	96.07 23	66.30 136	94.84 45	93.78 66	69.35 256	88.39 33	96.34 28	67.74 55	97.66 54	90.62 37	93.44 51	96.01 44
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	85.39 107	86.95 228	64.37 183	94.30 55	88.45 291	80.51 50	92.70 4	96.86 15	69.98 44	97.15 86	95.83 4	88.08 111	94.65 102

SD-MVS		87.49 28	87.49 31	87.50 42	93.60 56	68.82 69	93.90 74	92.63 121	76.86 114	87.90 35	95.76 41	66.17 66	97.63 56	89.06 46	91.48 78	96.05 42
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	85.30 111	87.10 225	64.19 190	94.41 52	88.14 300	80.24 58	92.54 5	96.97 10	69.52 46	97.17 83	95.89 3	88.51 107	94.56 105

dcpmvs_2		87.37 31	87.55 30	86.85 58	95.04 32	68.20 87	90.36 226	90.66 202	79.37 72	81.20 94	93.67 105	74.73 15	96.55 120	90.88 35	92.00 69	95.82 48

alignmvs		87.28 32	86.97 37	88.24 27	91.30 125	71.14 26	95.61 25	93.56 79	79.30 73	87.07 42	95.25 58	68.43 48	96.93 106	87.87 53	84.33 148	96.65 17

train_agg		87.21 33	87.42 32	86.60 68	94.18 41	67.28 109	94.16 59	93.51 81	71.87 207	85.52 57	95.33 51	68.19 50	97.27 80	89.09 45	94.90 22	95.25 76

MG-MVS		87.11 34	86.27 45	89.62 8	97.79 1	76.27 4	94.96 43	94.49 44	78.74 88	83.87 75	92.94 119	64.34 88	96.94 104	75.19 156	94.09 38	95.66 52

SF-MVS		87.03 35	87.09 35	86.84 59	92.70 84	67.45 107	93.64 90	93.76 69	70.78 240	86.25 47	96.44 26	66.98 59	97.79 47	88.68 49	94.56 34	95.28 72

CSCG		86.87 36	86.26 46	88.72 17	95.05 31	70.79 29	93.83 82	95.33 16	68.48 269	77.63 140	94.35 88	73.04 26	98.45 30	84.92 84	93.71 47	96.92 14

sasdasda		86.85 37	86.25 47	88.66 20	91.80 109	71.92 16	93.54 95	91.71 159	80.26 55	87.55 37	95.25 58	63.59 101	96.93 106	88.18 50	84.34 146	97.11 9

canonicalmvs		86.85 37	86.25 47	88.66 20	91.80 109	71.92 16	93.54 95	91.71 159	80.26 55	87.55 37	95.25 58	63.59 101	96.93 106	88.18 50	84.34 146	97.11 9

UBG		86.83 39	86.70 42	87.20 48	93.07 73	69.81 46	93.43 103	95.56 12	81.52 35	81.50 90	92.12 140	73.58 25	96.28 129	84.37 90	85.20 139	95.51 58

PHI-MVS		86.83 39	86.85 41	86.78 63	93.47 62	65.55 154	95.39 30	95.10 22	71.77 212	85.69 56	96.52 23	62.07 121	98.77 23	86.06 74	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 70	90.42 141	66.38 133	96.09 17	93.87 64	77.73 101	84.01 74	95.66 43	63.39 104	97.94 40	87.40 60	93.55 50	95.42 59
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 42	86.86 40	86.31 81	93.76 50	67.53 104	96.33 16	93.61 77	82.34 27	81.00 99	93.08 115	63.19 108	97.29 76	87.08 65	91.38 80	94.13 123

testing11		86.71 43	86.44 44	87.55 40	93.54 59	71.35 21	93.65 89	95.58 10	81.36 42	80.69 102	92.21 139	72.30 32	96.46 125	85.18 80	83.43 155	94.82 95

test_fmvsmconf_n		86.58 44	87.17 34	84.82 126	85.28 258	62.55 235	94.26 57	89.78 235	83.81 17	87.78 36	96.33 29	65.33 75	96.98 98	94.40 11	87.55 117	94.95 87

jason		86.40 45	86.17 49	87.11 51	86.16 243	70.54 32	95.71 24	92.19 137	82.00 30	84.58 67	94.34 89	61.86 123	95.53 169	87.76 54	90.89 86	95.27 73
jason: jason.
fmvsm_s_conf0.5_n		86.39 46	86.91 38	84.82 126	87.36 220	63.54 211	94.74 47	90.02 229	82.52 24	90.14 25	96.92 13	62.93 113	97.84 46	95.28 8	82.26 165	93.07 160

WTY-MVS		86.32 47	85.81 56	87.85 29	92.82 80	69.37 57	95.20 34	95.25 17	82.71 22	81.91 87	94.73 74	67.93 54	97.63 56	79.55 126	82.25 166	96.54 22

MSLP-MVS++		86.27 48	85.91 55	87.35 45	92.01 101	68.97 66	95.04 40	92.70 114	79.04 83	81.50 90	96.50 25	58.98 156	96.78 112	83.49 98	93.93 41	96.29 35

VNet		86.20 49	85.65 60	87.84 30	93.92 47	69.99 38	95.73 23	95.94 7	78.43 91	86.00 52	93.07 116	58.22 162	97.00 94	85.22 78	84.33 148	96.52 23

MVS_111021_HR		86.19 50	85.80 57	87.37 44	93.17 69	69.79 47	93.99 69	93.76 69	79.08 80	78.88 128	93.99 99	62.25 120	98.15 36	85.93 75	91.15 84	94.15 122

CS-MVS-test		86.14 51	87.01 36	83.52 172	92.63 86	59.36 296	95.49 27	91.92 146	80.09 59	85.46 59	95.53 47	61.82 125	95.77 152	86.77 69	93.37 52	95.41 60

ACMMP_NAP		86.05 52	85.80 57	86.80 62	91.58 115	67.53 104	91.79 170	93.49 84	74.93 140	84.61 66	95.30 53	59.42 149	97.92 41	86.13 72	94.92 20	94.94 88

testing99		86.01 53	85.47 61	87.63 38	93.62 55	71.25 23	93.47 101	95.23 18	80.42 53	80.60 104	91.95 144	71.73 37	96.50 123	80.02 123	82.22 167	95.13 79

ETV-MVS		86.01 53	86.11 50	85.70 99	90.21 146	67.02 118	93.43 103	91.92 146	81.21 44	84.13 73	94.07 98	60.93 133	95.63 160	89.28 43	89.81 96	94.46 114

testing91		85.93 55	85.31 64	87.78 32	93.59 57	71.47 19	93.50 98	95.08 25	80.26 55	80.53 105	91.93 145	70.43 41	96.51 122	80.32 121	82.13 169	95.37 63

APD-MVS		85.93 55	85.99 53	85.76 96	95.98 26	65.21 161	93.59 93	92.58 123	66.54 282	86.17 50	95.88 39	63.83 94	97.00 94	86.39 71	92.94 57	95.06 82
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 57	85.46 62	87.18 49	88.20 199	72.42 15	92.41 142	92.77 112	82.11 29	80.34 108	93.07 116	68.27 49	95.02 182	78.39 139	93.59 49	94.09 125

CS-MVS		85.80 58	86.65 43	83.27 180	92.00 102	58.92 300	95.31 31	91.86 151	79.97 60	84.82 65	95.40 49	62.26 119	95.51 170	86.11 73	92.08 68	95.37 63

fmvsm_s_conf0.5_n_a		85.75 59	86.09 51	84.72 133	85.73 252	63.58 208	93.79 83	89.32 253	81.42 40	90.21 23	96.91 14	62.41 118	97.67 51	94.48 10	80.56 184	92.90 166

test_fmvsmconf0.1_n		85.71 60	86.08 52	84.62 140	80.83 311	62.33 240	93.84 80	88.81 279	83.50 19	87.00 43	96.01 37	63.36 105	96.93 106	94.04 12	87.29 120	94.61 104

CDPH-MVS		85.71 60	85.46 62	86.46 74	94.75 34	67.19 111	93.89 75	92.83 111	70.90 236	83.09 79	95.28 54	63.62 99	97.36 71	80.63 118	94.18 37	94.84 92

casdiffmvs_mvg		85.66 62	85.18 66	87.09 52	88.22 198	69.35 58	93.74 86	91.89 149	81.47 36	80.10 110	91.45 154	64.80 83	96.35 127	87.23 63	87.69 115	95.58 55
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 63	85.93 54	84.68 136	82.95 295	63.48 213	94.03 68	89.46 247	81.69 33	89.86 26	96.74 20	61.85 124	97.75 49	94.74 9	82.01 171	92.81 168

MGCFI-Net		85.59 64	85.73 59	85.17 117	91.41 123	62.44 236	92.87 120	91.31 176	79.65 66	86.99 44	95.14 64	62.90 114	96.12 136	87.13 64	84.13 153	96.96 13

DeepC-MVS	77.85 3	85.52 65	85.24 65	86.37 78	88.80 181	66.64 127	92.15 149	93.68 75	81.07 45	76.91 150	93.64 106	62.59 116	98.44 31	85.50 76	92.84 59	94.03 129
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 66	84.87 72	86.84 59	88.25 196	69.07 62	93.04 114	91.76 156	81.27 43	80.84 101	92.07 142	64.23 89	96.06 142	84.98 83	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 67	85.08 68	86.06 84	93.09 72	65.65 150	93.89 75	93.41 89	73.75 160	79.94 112	94.68 76	60.61 136	98.03 38	82.63 103	93.72 46	94.52 110

MP-MVS-pluss		85.24 68	85.13 67	85.56 102	91.42 120	65.59 152	91.54 180	92.51 125	74.56 143	80.62 103	95.64 44	59.15 153	97.00 94	86.94 67	93.80 43	94.07 127
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 69	84.69 74	86.63 67	92.91 77	69.91 42	92.61 133	95.80 9	80.31 54	80.38 107	92.27 136	68.73 47	95.19 179	75.94 151	83.27 157	94.81 96

PAPR		85.15 70	84.47 75	87.18 49	96.02 25	68.29 81	91.85 168	93.00 106	76.59 121	79.03 124	95.00 65	61.59 126	97.61 58	78.16 140	89.00 103	95.63 53

MP-MVS		85.02 71	84.97 70	85.17 117	92.60 87	64.27 188	93.24 107	92.27 130	73.13 171	79.63 116	94.43 82	61.90 122	97.17 83	85.00 82	92.56 61	94.06 128
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 72	84.44 76	86.71 64	88.33 193	68.73 71	90.24 231	91.82 155	81.05 46	81.18 95	92.50 128	63.69 97	96.08 141	84.45 89	86.71 129	95.32 68

CHOSEN 1792x2688		84.98 73	83.45 90	89.57 11	89.94 151	75.14 6	92.07 155	92.32 128	81.87 31	75.68 159	88.27 202	60.18 139	98.60 27	80.46 120	90.27 94	94.96 86

MVSMamba_PlusPlus		84.97 74	83.65 84	88.93 14	90.17 147	74.04 8	87.84 278	92.69 116	62.18 317	81.47 92	87.64 216	71.47 38	96.28 129	84.69 86	94.74 31	96.47 28

EIA-MVS		84.84 75	84.88 71	84.69 135	91.30 125	62.36 239	93.85 77	92.04 141	79.45 69	79.33 121	94.28 92	62.42 117	96.35 127	80.05 122	91.25 83	95.38 62

fmvsm_s_conf0.1_n_a		84.76 76	84.84 73	84.53 142	80.23 321	63.50 212	92.79 122	88.73 282	80.46 51	89.84 27	96.65 22	60.96 132	97.57 61	93.80 13	80.14 186	92.53 175

HFP-MVS		84.73 77	84.40 77	85.72 98	93.75 52	65.01 167	93.50 98	93.19 96	72.19 196	79.22 122	94.93 68	59.04 154	97.67 51	81.55 109	92.21 64	94.49 113

MVS		84.66 78	82.86 105	90.06 2	90.93 132	74.56 7	87.91 276	95.54 13	68.55 267	72.35 201	94.71 75	59.78 145	98.90 20	81.29 115	94.69 32	96.74 16

GST-MVS		84.63 79	84.29 78	85.66 100	92.82 80	65.27 159	93.04 114	93.13 99	73.20 169	78.89 125	94.18 95	59.41 150	97.85 45	81.45 111	92.48 63	93.86 137

EC-MVSNet		84.53 80	85.04 69	83.01 184	89.34 163	61.37 260	94.42 51	91.09 188	77.91 98	83.24 77	94.20 94	58.37 160	95.40 171	85.35 77	91.41 79	92.27 185

ACMMPR		84.37 81	84.06 79	85.28 112	93.56 58	64.37 183	93.50 98	93.15 98	72.19 196	78.85 130	94.86 71	56.69 182	97.45 65	81.55 109	92.20 65	94.02 130

region2R		84.36 82	84.03 80	85.36 109	93.54 59	64.31 186	93.43 103	92.95 107	72.16 199	78.86 129	94.84 72	56.97 177	97.53 63	81.38 113	92.11 67	94.24 117

LFMVS		84.34 83	82.73 107	89.18 13	94.76 33	73.25 11	94.99 42	91.89 149	71.90 204	82.16 86	93.49 110	47.98 273	97.05 89	82.55 104	84.82 142	97.25 8

test_yl		84.28 84	83.16 98	87.64 34	94.52 37	69.24 59	95.78 18	95.09 23	69.19 259	81.09 96	92.88 122	57.00 175	97.44 66	81.11 116	81.76 173	96.23 38

DCV-MVSNet		84.28 84	83.16 98	87.64 34	94.52 37	69.24 59	95.78 18	95.09 23	69.19 259	81.09 96	92.88 122	57.00 175	97.44 66	81.11 116	81.76 173	96.23 38

diffmvs		84.28 84	83.83 81	85.61 101	87.40 218	68.02 91	90.88 209	89.24 256	80.54 49	81.64 89	92.52 127	59.83 144	94.52 205	87.32 61	85.11 140	94.29 115
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 84	83.36 96	87.02 55	92.22 94	67.74 97	84.65 304	94.50 43	79.15 77	82.23 85	87.93 211	66.88 60	96.94 104	80.53 119	82.20 168	96.39 33

ETVMVS		84.22 88	83.71 82	85.76 96	92.58 88	68.25 85	92.45 141	95.53 14	79.54 68	79.46 118	91.64 152	70.29 42	94.18 217	69.16 211	82.76 163	94.84 92

MAR-MVS		84.18 89	83.43 91	86.44 75	96.25 21	65.93 145	94.28 56	94.27 56	74.41 144	79.16 123	95.61 45	53.99 214	98.88 22	69.62 205	93.26 54	94.50 112
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 90	83.20 97	87.05 54	91.56 116	69.82 45	89.99 240	92.05 140	77.77 100	82.84 80	86.57 233	63.93 93	96.09 138	74.91 161	89.18 102	95.25 76

CANet_DTU		84.09 91	83.52 85	85.81 93	90.30 144	66.82 122	91.87 166	89.01 271	85.27 9	86.09 51	93.74 103	47.71 277	96.98 98	77.90 142	89.78 98	93.65 142

ET-MVSNet_ETH3D		84.01 92	83.15 100	86.58 70	90.78 137	70.89 28	94.74 47	94.62 40	81.44 39	58.19 332	93.64 106	73.64 24	92.35 282	82.66 102	78.66 201	96.50 27

PVSNet_Blended_VisFu		83.97 93	83.50 87	85.39 107	90.02 149	66.59 130	93.77 84	91.73 157	77.43 109	77.08 149	89.81 185	63.77 96	96.97 101	79.67 125	88.21 109	92.60 172

MTAPA		83.91 94	83.38 95	85.50 103	91.89 107	65.16 163	81.75 328	92.23 131	75.32 135	80.53 105	95.21 61	56.06 191	97.16 85	84.86 85	92.55 62	94.18 119

XVS		83.87 95	83.47 89	85.05 119	93.22 65	63.78 197	92.92 118	92.66 118	73.99 152	78.18 134	94.31 91	55.25 197	97.41 68	79.16 130	91.58 76	93.95 132

Effi-MVS+		83.82 96	82.76 106	86.99 56	89.56 159	69.40 53	91.35 191	86.12 325	72.59 183	83.22 78	92.81 125	59.60 147	96.01 146	81.76 108	87.80 114	95.56 56

test_fmvsmvis_n_1920		83.80 97	83.48 88	84.77 130	82.51 297	63.72 201	91.37 189	83.99 347	81.42 40	77.68 139	95.74 42	58.37 160	97.58 59	93.38 14	86.87 123	93.00 163

EI-MVSNet-Vis-set		83.77 98	83.67 83	84.06 157	92.79 83	63.56 209	91.76 173	94.81 31	79.65 66	77.87 137	94.09 96	63.35 106	97.90 42	79.35 128	79.36 193	90.74 211

MVSFormer		83.75 99	82.88 104	86.37 78	89.24 171	71.18 24	89.07 258	90.69 199	65.80 287	87.13 40	94.34 89	64.99 78	92.67 269	72.83 173	91.80 72	95.27 73

CP-MVS		83.71 100	83.40 94	84.65 137	93.14 70	63.84 195	94.59 49	92.28 129	71.03 234	77.41 143	94.92 69	55.21 200	96.19 133	81.32 114	90.70 88	93.91 134

test_fmvsmconf0.01_n		83.70 101	83.52 85	84.25 154	75.26 364	61.72 254	92.17 148	87.24 313	82.36 26	84.91 64	95.41 48	55.60 195	96.83 111	92.85 18	85.87 135	94.21 118

baseline2		83.68 102	83.42 93	84.48 145	87.37 219	66.00 142	90.06 235	95.93 8	79.71 65	69.08 238	90.39 172	77.92 6	96.28 129	78.91 134	81.38 177	91.16 207

thisisatest0515		83.41 103	82.49 111	86.16 83	89.46 162	68.26 83	93.54 95	94.70 36	74.31 147	75.75 157	90.92 162	72.62 29	96.52 121	69.64 203	81.50 176	93.71 140

PVSNet_BlendedMVS		83.38 104	83.43 91	83.22 181	93.76 50	67.53 104	94.06 63	93.61 77	79.13 78	81.00 99	85.14 248	63.19 108	97.29 76	87.08 65	73.91 237	84.83 306

test2506		83.29 105	82.92 103	84.37 149	88.39 191	63.18 221	92.01 158	91.35 175	77.66 103	78.49 133	91.42 155	64.58 86	95.09 181	73.19 169	89.23 100	94.85 89

PGM-MVS		83.25 106	82.70 108	84.92 122	92.81 82	64.07 192	90.44 222	92.20 135	71.28 228	77.23 146	94.43 82	55.17 201	97.31 75	79.33 129	91.38 80	93.37 148

HPM-MVS		83.25 106	82.95 102	84.17 155	92.25 93	62.88 230	90.91 206	91.86 151	70.30 245	77.12 147	93.96 100	56.75 180	96.28 129	82.04 106	91.34 82	93.34 149
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-UG-set		83.14 108	82.96 101	83.67 170	92.28 92	63.19 220	91.38 188	94.68 37	79.22 75	76.60 152	93.75 102	62.64 115	97.76 48	78.07 141	78.01 204	90.05 220

VDD-MVS		83.06 109	81.81 120	86.81 61	90.86 135	67.70 98	95.40 29	91.50 170	75.46 132	81.78 88	92.34 135	40.09 314	97.13 87	86.85 68	82.04 170	95.60 54

h-mvs33		83.01 110	82.56 110	84.35 150	89.34 163	62.02 246	92.72 125	93.76 69	81.45 37	82.73 82	92.25 138	60.11 140	97.13 87	87.69 55	62.96 316	93.91 134

PAPM_NR		82.97 111	81.84 119	86.37 78	94.10 44	66.76 125	87.66 282	92.84 110	69.96 249	74.07 178	93.57 108	63.10 111	97.50 64	70.66 198	90.58 90	94.85 89

mPP-MVS		82.96 112	82.44 112	84.52 143	92.83 78	62.92 228	92.76 123	91.85 153	71.52 224	75.61 162	94.24 93	53.48 222	96.99 97	78.97 133	90.73 87	93.64 143

SR-MVS		82.81 113	82.58 109	83.50 175	93.35 63	61.16 263	92.23 147	91.28 180	64.48 296	81.27 93	95.28 54	53.71 218	95.86 148	82.87 101	88.77 105	93.49 146

DP-MVS Recon		82.73 114	81.65 121	85.98 86	97.31 4	67.06 115	95.15 36	91.99 143	69.08 262	76.50 154	93.89 101	54.48 209	98.20 35	70.76 196	85.66 137	92.69 169

CLD-MVS		82.73 114	82.35 114	83.86 162	87.90 206	67.65 100	95.45 28	92.18 138	85.06 10	72.58 194	92.27 136	52.46 230	95.78 150	84.18 91	79.06 196	88.16 247
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	82.38 113	83.73 166	89.25 168	59.58 291	92.24 146	94.89 28	77.96 96	79.86 113	92.38 133	56.70 181	97.05 89	77.26 145	80.86 181	94.55 106

3Dnovator	73.91 6	82.69 117	80.82 134	88.31 26	89.57 158	71.26 22	92.60 134	94.39 51	78.84 85	67.89 258	92.48 131	48.42 268	98.52 28	68.80 216	94.40 36	95.15 78

RRT-MVS		82.61 118	81.16 125	86.96 57	91.10 129	68.75 70	87.70 281	92.20 135	76.97 112	72.68 190	87.10 227	51.30 242	96.41 126	83.56 97	87.84 113	95.74 50

MVSTER		82.47 119	82.05 115	83.74 164	92.68 85	69.01 64	91.90 165	93.21 93	79.83 61	72.14 202	85.71 244	74.72 16	94.72 193	75.72 152	72.49 247	87.50 253

TESTMET0.1,1		82.41 120	81.98 118	83.72 167	88.08 200	63.74 199	92.70 127	93.77 68	79.30 73	77.61 141	87.57 218	58.19 163	94.08 221	73.91 167	86.68 130	93.33 151

CostFormer		82.33 121	81.15 126	85.86 91	89.01 176	68.46 77	82.39 325	93.01 104	75.59 130	80.25 109	81.57 290	72.03 35	94.96 185	79.06 132	77.48 212	94.16 121

API-MVS		82.28 122	80.53 142	87.54 41	96.13 22	70.59 31	93.63 91	91.04 194	65.72 289	75.45 164	92.83 124	56.11 190	98.89 21	64.10 260	89.75 99	93.15 156

IB-MVS	77.80 4	82.18 123	80.46 144	87.35 45	89.14 173	70.28 35	95.59 26	95.17 21	78.85 84	70.19 226	85.82 242	70.66 40	97.67 51	72.19 185	66.52 288	94.09 125
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		82.16 124	81.12 127	85.26 114	86.42 236	68.72 72	92.59 136	90.44 209	73.12 172	84.20 70	94.36 84	38.04 327	95.73 154	84.12 92	86.81 124	91.33 200

xiu_mvs_v1_base		82.16 124	81.12 127	85.26 114	86.42 236	68.72 72	92.59 136	90.44 209	73.12 172	84.20 70	94.36 84	38.04 327	95.73 154	84.12 92	86.81 124	91.33 200

xiu_mvs_v1_base_debi		82.16 124	81.12 127	85.26 114	86.42 236	68.72 72	92.59 136	90.44 209	73.12 172	84.20 70	94.36 84	38.04 327	95.73 154	84.12 92	86.81 124	91.33 200

3Dnovator+	73.60 7	82.10 127	80.60 141	86.60 68	90.89 134	66.80 124	95.20 34	93.44 86	74.05 151	67.42 265	92.49 130	49.46 258	97.65 55	70.80 195	91.68 74	95.33 66

MVS_111021_LR		82.02 128	81.52 122	83.51 174	88.42 189	62.88 230	89.77 243	88.93 275	76.78 117	75.55 163	93.10 113	50.31 249	95.38 173	83.82 96	87.02 122	92.26 186

PMMVS		81.98 129	82.04 116	81.78 218	89.76 155	56.17 326	91.13 202	90.69 199	77.96 96	80.09 111	93.57 108	46.33 287	94.99 184	81.41 112	87.46 118	94.17 120

baseline1		81.84 130	81.03 131	84.28 153	91.60 114	66.62 128	91.08 203	91.66 164	81.87 31	74.86 169	91.67 151	69.98 44	94.92 188	71.76 188	64.75 303	91.29 205

EPP-MVSNet		81.79 131	81.52 122	82.61 194	88.77 182	60.21 283	93.02 116	93.66 76	68.52 268	72.90 188	90.39 172	72.19 34	94.96 185	74.93 160	79.29 195	92.67 170

WBMVS		81.67 132	80.98 133	83.72 167	93.07 73	69.40 53	94.33 54	93.05 102	76.84 115	72.05 204	84.14 259	74.49 18	93.88 235	72.76 176	68.09 276	87.88 249

test_vis1_n_1920		81.66 133	82.01 117	80.64 245	82.24 299	55.09 334	94.76 46	86.87 315	81.67 34	84.40 69	94.63 77	38.17 324	94.67 197	91.98 27	83.34 156	92.16 189

APD-MVS_3200maxsize		81.64 134	81.32 124	82.59 195	92.36 90	58.74 302	91.39 186	91.01 195	63.35 305	79.72 115	94.62 78	51.82 233	96.14 135	79.71 124	87.93 112	92.89 167

mvsmamba		81.55 135	80.72 136	84.03 159	91.42 120	66.93 120	83.08 319	89.13 264	78.55 90	67.50 263	87.02 228	51.79 235	90.07 322	87.48 58	90.49 92	95.10 81

ACMMP		81.49 136	80.67 138	83.93 161	91.71 112	62.90 229	92.13 150	92.22 134	71.79 211	71.68 210	93.49 110	50.32 248	96.96 102	78.47 138	84.22 152	91.93 192
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		81.43 137	80.74 135	83.52 172	86.26 240	64.45 177	92.09 153	90.65 203	75.83 128	73.95 180	89.81 185	63.97 92	92.91 259	71.27 191	82.82 160	93.20 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 138	79.99 149	85.46 104	90.39 143	68.40 78	86.88 293	90.61 204	74.41 144	70.31 225	84.67 253	63.79 95	92.32 283	73.13 170	85.70 136	95.67 51

ECVR-MVS		81.29 139	80.38 145	84.01 160	88.39 191	61.96 248	92.56 139	86.79 317	77.66 103	76.63 151	91.42 155	46.34 286	95.24 178	74.36 165	89.23 100	94.85 89

thisisatest0530		81.15 140	80.07 146	84.39 148	88.26 195	65.63 151	91.40 184	94.62 40	71.27 229	70.93 216	89.18 191	72.47 30	96.04 143	65.62 249	76.89 218	91.49 196

Fast-Effi-MVS+		81.14 141	80.01 148	84.51 144	90.24 145	65.86 146	94.12 62	89.15 262	73.81 159	75.37 165	88.26 203	57.26 170	94.53 204	66.97 234	84.92 141	93.15 156

HQP-MVS		81.14 141	80.64 139	82.64 193	87.54 214	63.66 206	94.06 63	91.70 162	79.80 62	74.18 174	90.30 174	51.63 238	95.61 162	77.63 143	78.90 197	88.63 238

hse-mvs2		81.12 143	81.11 130	81.16 232	86.52 235	57.48 315	89.40 251	91.16 183	81.45 37	82.73 82	90.49 170	60.11 140	94.58 198	87.69 55	60.41 343	91.41 199

SR-MVS-dyc-post		81.06 144	80.70 137	82.15 209	92.02 99	58.56 304	90.90 207	90.45 206	62.76 312	78.89 125	94.46 80	51.26 243	95.61 162	78.77 136	86.77 127	92.28 182

HyFIR lowres test		81.03 145	79.56 156	85.43 105	87.81 210	68.11 89	90.18 232	90.01 230	70.65 242	72.95 187	86.06 240	63.61 100	94.50 206	75.01 159	79.75 190	93.67 141

nrg030		80.93 146	79.86 151	84.13 156	83.69 284	68.83 68	93.23 108	91.20 181	75.55 131	75.06 167	88.22 206	63.04 112	94.74 192	81.88 107	66.88 285	88.82 236

Vis-MVSNet		80.92 147	79.98 150	83.74 164	88.48 186	61.80 250	93.44 102	88.26 299	73.96 155	77.73 138	91.76 148	49.94 253	94.76 190	65.84 246	90.37 93	94.65 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 148	80.02 147	83.33 178	87.87 207	60.76 271	92.62 132	86.86 316	77.86 99	75.73 158	91.39 157	46.35 285	94.70 196	72.79 175	88.68 106	94.52 110

UWE-MVS		80.81 149	81.01 132	80.20 254	89.33 165	57.05 320	91.91 164	94.71 35	75.67 129	75.01 168	89.37 189	63.13 110	91.44 306	67.19 231	82.80 162	92.12 190

1314		80.70 150	78.95 167	85.94 88	87.77 212	67.56 102	87.91 276	92.55 124	72.17 198	67.44 264	93.09 114	50.27 250	97.04 92	71.68 190	87.64 116	93.23 153

tpmrst		80.57 151	79.14 166	84.84 125	90.10 148	68.28 82	81.70 329	89.72 242	77.63 105	75.96 156	79.54 322	64.94 80	92.71 266	75.43 154	77.28 215	93.55 144

1112_ss		80.56 152	79.83 152	82.77 188	88.65 183	60.78 269	92.29 144	88.36 293	72.58 184	72.46 198	94.95 66	65.09 77	93.42 246	66.38 240	77.71 206	94.10 124

VDDNet		80.50 153	78.26 175	87.21 47	86.19 241	69.79 47	94.48 50	91.31 176	60.42 331	79.34 120	90.91 163	38.48 322	96.56 119	82.16 105	81.05 179	95.27 73

BH-w/o		80.49 154	79.30 163	84.05 158	90.83 136	64.36 185	93.60 92	89.42 250	74.35 146	69.09 237	90.15 180	55.23 199	95.61 162	64.61 257	86.43 133	92.17 188

test_cas_vis1_n_1920		80.45 155	80.61 140	79.97 263	78.25 347	57.01 322	94.04 67	88.33 294	79.06 82	82.81 81	93.70 104	38.65 319	91.63 298	90.82 36	79.81 188	91.27 206

TAMVS		80.37 156	79.45 159	83.13 183	85.14 261	63.37 214	91.23 197	90.76 198	74.81 142	72.65 192	88.49 197	60.63 135	92.95 254	69.41 207	81.95 172	93.08 159

HQP_MVS		80.34 157	79.75 153	82.12 211	86.94 229	62.42 237	93.13 110	91.31 176	78.81 86	72.53 195	89.14 193	50.66 246	95.55 167	76.74 146	78.53 202	88.39 244

SDMVSNet		80.26 158	78.88 168	84.40 147	89.25 168	67.63 101	85.35 300	93.02 103	76.77 118	70.84 217	87.12 225	47.95 274	96.09 138	85.04 81	74.55 228	89.48 230

HPM-MVS_fast		80.25 159	79.55 158	82.33 201	91.55 117	59.95 286	91.32 193	89.16 261	65.23 293	74.71 171	93.07 116	47.81 276	95.74 153	74.87 163	88.23 108	91.31 204

ab-mvs		80.18 160	78.31 174	85.80 94	88.44 188	65.49 157	83.00 322	92.67 117	71.82 210	77.36 144	85.01 249	54.50 206	96.59 116	76.35 150	75.63 225	95.32 68

IS-MVSNet		80.14 161	79.41 160	82.33 201	87.91 205	60.08 285	91.97 162	88.27 297	72.90 179	71.44 213	91.73 150	61.44 127	93.66 241	62.47 274	86.53 131	93.24 152

test-LLR		80.10 162	79.56 156	81.72 220	86.93 231	61.17 261	92.70 127	91.54 167	71.51 225	75.62 160	86.94 229	53.83 215	92.38 279	72.21 183	84.76 144	91.60 194

PVSNet	73.49 8	80.05 163	78.63 170	84.31 151	90.92 133	64.97 168	92.47 140	91.05 193	79.18 76	72.43 199	90.51 169	37.05 339	94.06 223	68.06 220	86.00 134	93.90 136

UA-Net		80.02 164	79.65 154	81.11 234	89.33 165	57.72 311	86.33 297	89.00 274	77.44 108	81.01 98	89.15 192	59.33 151	95.90 147	61.01 281	84.28 150	89.73 226

test-mter		79.96 165	79.38 162	81.72 220	86.93 231	61.17 261	92.70 127	91.54 167	73.85 157	75.62 160	86.94 229	49.84 255	92.38 279	72.21 183	84.76 144	91.60 194

QAPM		79.95 166	77.39 192	87.64 34	89.63 157	71.41 20	93.30 106	93.70 74	65.34 292	67.39 267	91.75 149	47.83 275	98.96 16	57.71 297	89.81 96	92.54 174

UGNet		79.87 167	78.68 169	83.45 177	89.96 150	61.51 257	92.13 150	90.79 197	76.83 116	78.85 130	86.33 237	38.16 325	96.17 134	67.93 223	87.17 121	92.67 170
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		79.80 168	77.95 181	85.34 110	88.28 194	68.26 83	81.56 331	91.42 173	70.11 247	77.59 142	80.50 308	67.40 57	94.26 215	67.34 228	77.35 213	93.51 145

thres200		79.66 169	78.33 173	83.66 171	92.54 89	65.82 148	93.06 112	96.31 3	74.90 141	73.30 184	88.66 195	59.67 146	95.61 162	47.84 336	78.67 200	89.56 229

CPTT-MVS		79.59 170	79.16 165	80.89 243	91.54 118	59.80 288	92.10 152	88.54 290	60.42 331	72.96 186	93.28 112	48.27 269	92.80 263	78.89 135	86.50 132	90.06 219

Test_1112_low_res		79.56 171	78.60 171	82.43 197	88.24 197	60.39 280	92.09 153	87.99 304	72.10 200	71.84 206	87.42 220	64.62 85	93.04 250	65.80 247	77.30 214	93.85 138

tttt0517		79.50 172	78.53 172	82.41 200	87.22 222	61.43 259	89.75 244	94.76 32	69.29 257	67.91 256	88.06 210	72.92 27	95.63 160	62.91 270	73.90 238	90.16 218

FIs		79.47 173	79.41 160	79.67 270	85.95 246	59.40 293	91.68 177	93.94 63	78.06 95	68.96 242	88.28 201	66.61 63	91.77 294	66.20 243	74.99 227	87.82 250

BH-RMVSNet		79.46 174	77.65 184	84.89 123	91.68 113	65.66 149	93.55 94	88.09 302	72.93 176	73.37 183	91.12 161	46.20 289	96.12 136	56.28 302	85.61 138	92.91 165

PCF-MVS	73.15 9	79.29 175	77.63 185	84.29 152	86.06 244	65.96 144	87.03 289	91.10 187	69.86 251	69.79 233	90.64 165	57.54 169	96.59 116	64.37 259	82.29 164	90.32 216
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 176	79.57 155	78.24 290	88.46 187	52.29 345	90.41 224	89.12 265	74.24 148	69.13 236	91.91 146	65.77 71	90.09 321	59.00 293	88.09 110	92.33 179

114514_t		79.17 177	77.67 183	83.68 169	95.32 29	65.53 155	92.85 121	91.60 166	63.49 303	67.92 255	90.63 167	46.65 282	95.72 158	67.01 233	83.54 154	89.79 224

FA-MVS(test-final)		79.12 178	77.23 194	84.81 129	90.54 139	63.98 194	81.35 334	91.71 159	71.09 233	74.85 170	82.94 271	52.85 226	97.05 89	67.97 221	81.73 175	93.41 147

VPA-MVSNet		79.03 179	78.00 179	82.11 214	85.95 246	64.48 176	93.22 109	94.66 38	75.05 139	74.04 179	84.95 250	52.17 232	93.52 243	74.90 162	67.04 284	88.32 246

OPM-MVS		79.00 180	78.09 177	81.73 219	83.52 287	63.83 196	91.64 179	90.30 216	76.36 124	71.97 205	89.93 184	46.30 288	95.17 180	75.10 157	77.70 207	86.19 279
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 181	78.22 176	81.25 229	85.33 256	62.73 233	89.53 248	93.21 93	72.39 191	72.14 202	90.13 181	60.99 130	94.72 193	67.73 225	72.49 247	86.29 276

AdaColmap		78.94 182	77.00 198	84.76 131	96.34 17	65.86 146	92.66 131	87.97 306	62.18 317	70.56 219	92.37 134	43.53 302	97.35 72	64.50 258	82.86 159	91.05 209

GeoE		78.90 183	77.43 188	83.29 179	88.95 177	62.02 246	92.31 143	86.23 323	70.24 246	71.34 214	89.27 190	54.43 210	94.04 226	63.31 266	80.81 183	93.81 139

miper_enhance_ethall		78.86 184	77.97 180	81.54 224	88.00 204	65.17 162	91.41 182	89.15 262	75.19 137	68.79 245	83.98 262	67.17 58	92.82 261	72.73 177	65.30 294	86.62 273

VPNet		78.82 185	77.53 187	82.70 191	84.52 271	66.44 132	93.93 72	92.23 131	80.46 51	72.60 193	88.38 200	49.18 262	93.13 249	72.47 181	63.97 313	88.55 241

EPNet_dtu		78.80 186	79.26 164	77.43 298	88.06 201	49.71 359	91.96 163	91.95 145	77.67 102	76.56 153	91.28 159	58.51 158	90.20 319	56.37 301	80.95 180	92.39 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 187	77.43 188	82.88 186	92.21 95	64.49 174	92.05 156	96.28 4	73.48 166	71.75 208	88.26 203	60.07 142	95.32 174	45.16 347	77.58 209	88.83 234

TR-MVS		78.77 188	77.37 193	82.95 185	90.49 140	60.88 267	93.67 88	90.07 225	70.08 248	74.51 172	91.37 158	45.69 291	95.70 159	60.12 287	80.32 185	92.29 181

thres400		78.68 189	77.43 188	82.43 197	92.21 95	64.49 174	92.05 156	96.28 4	73.48 166	71.75 208	88.26 203	60.07 142	95.32 174	45.16 347	77.58 209	87.48 254

BH-untuned		78.68 189	77.08 195	83.48 176	89.84 152	63.74 199	92.70 127	88.59 288	71.57 222	66.83 274	88.65 196	51.75 236	95.39 172	59.03 292	84.77 143	91.32 203

OMC-MVS		78.67 191	77.91 182	80.95 241	85.76 251	57.40 317	88.49 267	88.67 285	73.85 157	72.43 199	92.10 141	49.29 261	94.55 203	72.73 177	77.89 205	90.91 210

tpm		78.58 192	77.03 196	83.22 181	85.94 248	64.56 172	83.21 318	91.14 186	78.31 92	73.67 181	79.68 320	64.01 91	92.09 288	66.07 244	71.26 257	93.03 161

OpenMVS	70.45 11	78.54 193	75.92 212	86.41 77	85.93 249	71.68 18	92.74 124	92.51 125	66.49 283	64.56 289	91.96 143	43.88 301	98.10 37	54.61 307	90.65 89	89.44 232

EPMVS		78.49 194	75.98 211	86.02 85	91.21 127	69.68 51	80.23 343	91.20 181	75.25 136	72.48 197	78.11 331	54.65 205	93.69 240	57.66 298	83.04 158	94.69 98

AUN-MVS		78.37 195	77.43 188	81.17 231	86.60 234	57.45 316	89.46 250	91.16 183	74.11 150	74.40 173	90.49 170	55.52 196	94.57 200	74.73 164	60.43 342	91.48 197

thres100view900		78.37 195	77.01 197	82.46 196	91.89 107	63.21 219	91.19 201	96.33 1	72.28 194	70.45 222	87.89 212	60.31 137	95.32 174	45.16 347	77.58 209	88.83 234

GA-MVS		78.33 197	76.23 207	84.65 137	83.65 285	66.30 136	91.44 181	90.14 223	76.01 126	70.32 224	84.02 261	42.50 306	94.72 193	70.98 193	77.00 217	92.94 164

cascas		78.18 198	75.77 214	85.41 106	87.14 224	69.11 61	92.96 117	91.15 185	66.71 281	70.47 220	86.07 239	37.49 333	96.48 124	70.15 201	79.80 189	90.65 212

UniMVSNet_NR-MVSNet		78.15 199	77.55 186	79.98 261	84.46 273	60.26 281	92.25 145	93.20 95	77.50 107	68.88 243	86.61 232	66.10 67	92.13 286	66.38 240	62.55 320	87.54 252

thres600view7		78.00 200	76.66 202	82.03 216	91.93 104	63.69 204	91.30 194	96.33 1	72.43 189	70.46 221	87.89 212	60.31 137	94.92 188	42.64 359	76.64 219	87.48 254

FC-MVSNet-test		77.99 201	78.08 178	77.70 293	84.89 266	55.51 331	90.27 229	93.75 72	76.87 113	66.80 275	87.59 217	65.71 72	90.23 318	62.89 271	73.94 236	87.37 257

Anonymous202405211		77.96 202	75.33 220	85.87 90	93.73 53	64.52 173	94.85 44	85.36 332	62.52 315	76.11 155	90.18 177	29.43 367	97.29 76	68.51 218	77.24 216	95.81 49

cl22		77.94 203	76.78 200	81.42 226	87.57 213	64.93 170	90.67 216	88.86 278	72.45 188	67.63 262	82.68 275	64.07 90	92.91 259	71.79 186	65.30 294	86.44 274

XXY-MVS		77.94 203	76.44 204	82.43 197	82.60 296	64.44 178	92.01 158	91.83 154	73.59 165	70.00 229	85.82 242	54.43 210	94.76 190	69.63 204	68.02 278	88.10 248

MS-PatchMatch		77.90 205	76.50 203	82.12 211	85.99 245	69.95 41	91.75 175	92.70 114	73.97 154	62.58 310	84.44 257	41.11 311	95.78 150	63.76 263	92.17 66	80.62 353

FMVSNet3		77.73 206	76.04 210	82.80 187	91.20 128	68.99 65	91.87 166	91.99 143	73.35 168	67.04 270	83.19 270	56.62 183	92.14 285	59.80 289	69.34 264	87.28 260

miper_ehance_all_eth		77.60 207	76.44 204	81.09 238	85.70 253	64.41 181	90.65 217	88.64 287	72.31 192	67.37 268	82.52 276	64.77 84	92.64 272	70.67 197	65.30 294	86.24 278

UniMVSNet (Re)		77.58 208	76.78 200	79.98 261	84.11 279	60.80 268	91.76 173	93.17 97	76.56 122	69.93 232	84.78 252	63.32 107	92.36 281	64.89 256	62.51 322	86.78 268

PatchmatchNet		77.46 209	74.63 227	85.96 87	89.55 160	70.35 34	79.97 348	89.55 245	72.23 195	70.94 215	76.91 343	57.03 173	92.79 264	54.27 309	81.17 178	94.74 97
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 210	75.65 216	82.73 189	80.38 317	67.13 114	91.85 168	90.23 220	75.09 138	69.37 234	83.39 268	53.79 217	94.44 207	71.77 187	65.00 300	86.63 272

CHOSEN 280x420		77.35 211	76.95 199	78.55 285	87.07 226	62.68 234	69.71 380	82.95 354	68.80 264	71.48 212	87.27 224	66.03 68	84.00 364	76.47 149	82.81 161	88.95 233

PS-MVSNAJss		77.26 212	76.31 206	80.13 256	80.64 315	59.16 298	90.63 220	91.06 192	72.80 180	68.58 249	84.57 255	53.55 219	93.96 231	72.97 171	71.96 251	87.27 261

gg-mvs-nofinetune		77.18 213	74.31 234	85.80 94	91.42 120	68.36 79	71.78 374	94.72 34	49.61 374	77.12 147	45.92 400	77.41 8	93.98 230	67.62 226	93.16 55	95.05 83

WB-MVSnew		77.14 214	76.18 209	80.01 260	86.18 242	63.24 217	91.26 195	94.11 60	71.72 214	73.52 182	87.29 223	45.14 296	93.00 252	56.98 299	79.42 191	83.80 314

MVP-Stereo		77.12 215	76.23 207	79.79 268	81.72 304	66.34 135	89.29 252	90.88 196	70.56 243	62.01 313	82.88 272	49.34 259	94.13 218	65.55 251	93.80 43	78.88 367
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 216	75.37 218	82.20 207	89.25 168	62.11 245	82.06 326	89.09 267	76.77 118	70.84 217	87.12 225	41.43 310	95.01 183	67.23 230	74.55 228	89.48 230

MonoMVSNet		76.99 217	75.08 223	82.73 189	83.32 289	63.24 217	86.47 296	86.37 319	79.08 80	66.31 277	79.30 324	49.80 256	91.72 295	79.37 127	65.70 292	93.23 153

dmvs_re		76.93 218	75.36 219	81.61 222	87.78 211	60.71 274	80.00 347	87.99 304	79.42 70	69.02 240	89.47 188	46.77 280	94.32 209	63.38 265	74.45 231	89.81 223

X-MVStestdata		76.86 219	74.13 238	85.05 119	93.22 65	63.78 197	92.92 118	92.66 118	73.99 152	78.18 134	10.19 415	55.25 197	97.41 68	79.16 130	91.58 76	93.95 132

DU-MVS		76.86 219	75.84 213	79.91 264	82.96 293	60.26 281	91.26 195	91.54 167	76.46 123	68.88 243	86.35 235	56.16 188	92.13 286	66.38 240	62.55 320	87.35 258

Anonymous20240529		76.84 221	74.15 237	84.88 124	91.02 130	64.95 169	93.84 80	91.09 188	53.57 362	73.00 185	87.42 220	35.91 343	97.32 74	69.14 212	72.41 249	92.36 178

c3_l		76.83 222	75.47 217	80.93 242	85.02 264	64.18 191	90.39 225	88.11 301	71.66 215	66.65 276	81.64 288	63.58 103	92.56 273	69.31 209	62.86 317	86.04 284

WR-MVS		76.76 223	75.74 215	79.82 267	84.60 269	62.27 243	92.60 134	92.51 125	76.06 125	67.87 259	85.34 246	56.76 179	90.24 317	62.20 275	63.69 315	86.94 266

v1144		76.73 224	74.88 224	82.27 203	80.23 321	66.60 129	91.68 177	90.21 222	73.69 162	69.06 239	81.89 283	52.73 228	94.40 208	69.21 210	65.23 297	85.80 290

IterMVS-LS		76.49 225	75.18 222	80.43 248	84.49 272	62.74 232	90.64 218	88.80 280	72.40 190	65.16 284	81.72 286	60.98 131	92.27 284	67.74 224	64.65 305	86.29 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 226	74.55 230	82.19 208	79.14 335	67.82 95	90.26 230	89.42 250	73.75 160	68.63 248	81.89 283	51.31 241	94.09 220	71.69 189	64.84 301	84.66 307

v148		76.19 227	74.47 232	81.36 227	80.05 323	64.44 178	91.75 175	90.23 220	73.68 163	67.13 269	80.84 303	55.92 193	93.86 238	68.95 214	61.73 331	85.76 293

Effi-MVS+-dtu		76.14 228	75.28 221	78.72 284	83.22 290	55.17 333	89.87 241	87.78 307	75.42 133	67.98 254	81.43 292	45.08 297	92.52 275	75.08 158	71.63 252	88.48 242

cl____		76.07 229	74.67 225	80.28 251	85.15 260	61.76 252	90.12 233	88.73 282	71.16 230	65.43 281	81.57 290	61.15 128	92.95 254	66.54 237	62.17 324	86.13 282

DIV-MVS_self_test		76.07 229	74.67 225	80.28 251	85.14 261	61.75 253	90.12 233	88.73 282	71.16 230	65.42 282	81.60 289	61.15 128	92.94 258	66.54 237	62.16 326	86.14 280

FMVSNet2		76.07 229	74.01 240	82.26 205	88.85 178	67.66 99	91.33 192	91.61 165	70.84 237	65.98 278	82.25 279	48.03 270	92.00 290	58.46 294	68.73 272	87.10 263

v144192		76.05 232	74.03 239	82.12 211	79.50 329	66.55 131	91.39 186	89.71 243	72.30 193	68.17 252	81.33 295	51.75 236	94.03 228	67.94 222	64.19 308	85.77 291

NR-MVSNet		76.05 232	74.59 228	80.44 247	82.96 293	62.18 244	90.83 211	91.73 157	77.12 111	60.96 316	86.35 235	59.28 152	91.80 293	60.74 282	61.34 335	87.35 258

v1192		75.98 234	73.92 241	82.15 209	79.73 325	66.24 138	91.22 198	89.75 237	72.67 182	68.49 250	81.42 293	49.86 254	94.27 213	67.08 232	65.02 299	85.95 287

FE-MVS		75.97 235	73.02 251	84.82 126	89.78 153	65.56 153	77.44 359	91.07 191	64.55 295	72.66 191	79.85 318	46.05 290	96.69 114	54.97 306	80.82 182	92.21 187

eth_miper_zixun_eth		75.96 236	74.40 233	80.66 244	84.66 268	63.02 223	89.28 253	88.27 297	71.88 206	65.73 279	81.65 287	59.45 148	92.81 262	68.13 219	60.53 340	86.14 280

TranMVSNet+NR-MVSNet		75.86 237	74.52 231	79.89 265	82.44 298	60.64 277	91.37 189	91.37 174	76.63 120	67.65 261	86.21 238	52.37 231	91.55 300	61.84 277	60.81 338	87.48 254

SCA		75.82 238	72.76 254	85.01 121	86.63 233	70.08 37	81.06 336	89.19 259	71.60 221	70.01 228	77.09 341	45.53 292	90.25 314	60.43 284	73.27 240	94.68 99

LPG-MVS_test		75.82 238	74.58 229	79.56 274	84.31 276	59.37 294	90.44 222	89.73 240	69.49 254	64.86 285	88.42 198	38.65 319	94.30 211	72.56 179	72.76 244	85.01 304

GBi-Net		75.65 240	73.83 242	81.10 235	88.85 178	65.11 164	90.01 237	90.32 212	70.84 237	67.04 270	80.25 313	48.03 270	91.54 301	59.80 289	69.34 264	86.64 269

test1		75.65 240	73.83 242	81.10 235	88.85 178	65.11 164	90.01 237	90.32 212	70.84 237	67.04 270	80.25 313	48.03 270	91.54 301	59.80 289	69.34 264	86.64 269

v1921920		75.63 242	73.49 247	82.06 215	79.38 330	66.35 134	91.07 205	89.48 246	71.98 201	67.99 253	81.22 298	49.16 264	93.90 234	66.56 236	64.56 306	85.92 289

ACMP	71.68 10	75.58 243	74.23 236	79.62 272	84.97 265	59.64 289	90.80 212	89.07 269	70.39 244	62.95 306	87.30 222	38.28 323	93.87 236	72.89 172	71.45 255	85.36 300
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 244	73.26 249	81.61 222	80.67 314	66.82 122	89.54 247	89.27 255	71.65 216	63.30 302	80.30 312	54.99 203	94.06 223	67.33 229	62.33 323	83.94 312

tpm cat1		75.30 245	72.21 263	84.58 141	88.52 184	67.77 96	78.16 357	88.02 303	61.88 323	68.45 251	76.37 347	60.65 134	94.03 228	53.77 312	74.11 234	91.93 192

PLC	68.80 14	75.23 246	73.68 245	79.86 266	92.93 76	58.68 303	90.64 218	88.30 295	60.90 328	64.43 293	90.53 168	42.38 307	94.57 200	56.52 300	76.54 220	86.33 275
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 247	72.98 252	81.88 217	79.20 332	66.00 142	90.75 214	89.11 266	71.63 220	67.41 266	81.22 298	47.36 278	93.87 236	65.46 252	64.72 304	85.77 291

Fast-Effi-MVS+-dtu		75.04 248	73.37 248	80.07 257	80.86 310	59.52 292	91.20 200	85.38 331	71.90 204	65.20 283	84.84 251	41.46 309	92.97 253	66.50 239	72.96 243	87.73 251

dp		75.01 249	72.09 264	83.76 163	89.28 167	66.22 139	79.96 349	89.75 237	71.16 230	67.80 260	77.19 340	51.81 234	92.54 274	50.39 320	71.44 256	92.51 176

TAPA-MVS	70.22 12	74.94 250	73.53 246	79.17 279	90.40 142	52.07 346	89.19 256	89.61 244	62.69 314	70.07 227	92.67 126	48.89 267	94.32 209	38.26 373	79.97 187	91.12 208
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 251	72.54 260	81.46 225	80.33 319	66.71 126	89.15 257	89.08 268	70.94 235	63.08 305	79.86 317	52.52 229	94.04 226	65.70 248	62.17 324	83.64 315

XVG-OURS-SEG-HR		74.70 252	73.08 250	79.57 273	78.25 347	57.33 318	80.49 339	87.32 310	63.22 307	68.76 246	90.12 183	44.89 298	91.59 299	70.55 199	74.09 235	89.79 224

ACMM	69.62 13	74.34 253	72.73 256	79.17 279	84.25 278	57.87 309	90.36 226	89.93 231	63.17 309	65.64 280	86.04 241	37.79 331	94.10 219	65.89 245	71.52 254	85.55 296
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 254	72.30 262	80.32 249	91.49 119	61.66 255	90.85 210	80.72 360	56.67 354	63.85 297	90.64 165	46.75 281	90.84 309	53.79 311	75.99 224	88.47 243

XVG-OURS		74.25 255	72.46 261	79.63 271	78.45 345	57.59 314	80.33 341	87.39 309	63.86 300	68.76 246	89.62 187	40.50 313	91.72 295	69.00 213	74.25 233	89.58 227

test_fmvs1		74.07 256	73.69 244	75.22 316	78.91 339	47.34 372	89.06 260	74.69 375	63.68 302	79.41 119	91.59 153	24.36 377	87.77 341	85.22 78	76.26 222	90.55 215

CVMVSNet		74.04 257	74.27 235	73.33 331	85.33 256	43.94 385	89.53 248	88.39 292	54.33 361	70.37 223	90.13 181	49.17 263	84.05 362	61.83 278	79.36 193	91.99 191

Baseline_NR-MVSNet		73.99 258	72.83 253	77.48 297	80.78 312	59.29 297	91.79 170	84.55 340	68.85 263	68.99 241	80.70 304	56.16 188	92.04 289	62.67 272	60.98 337	81.11 347

pmmvs4		73.92 259	71.81 268	80.25 253	79.17 333	65.24 160	87.43 285	87.26 312	67.64 275	63.46 300	83.91 263	48.96 266	91.53 304	62.94 269	65.49 293	83.96 311

D2MVS		73.80 260	72.02 265	79.15 281	79.15 334	62.97 224	88.58 266	90.07 225	72.94 175	59.22 326	78.30 328	42.31 308	92.70 268	65.59 250	72.00 250	81.79 342

CR-MVSNet		73.79 261	70.82 276	82.70 191	83.15 291	67.96 92	70.25 377	84.00 345	73.67 164	69.97 230	72.41 363	57.82 166	89.48 326	52.99 315	73.13 241	90.64 213

test_djsdf		73.76 262	72.56 259	77.39 299	77.00 357	53.93 339	89.07 258	90.69 199	65.80 287	63.92 295	82.03 282	43.14 305	92.67 269	72.83 173	68.53 273	85.57 295

pmmvs5		73.35 263	71.52 270	78.86 283	78.64 343	60.61 278	91.08 203	86.90 314	67.69 272	63.32 301	83.64 264	44.33 300	90.53 311	62.04 276	66.02 290	85.46 298

Anonymous20231211		73.08 264	70.39 280	81.13 233	90.62 138	63.33 215	91.40 184	90.06 227	51.84 367	64.46 292	80.67 306	36.49 341	94.07 222	63.83 262	64.17 309	85.98 286

tt0805		73.07 265	70.73 277	80.07 257	78.37 346	57.05 320	87.78 279	92.18 138	61.23 327	67.04 270	86.49 234	31.35 361	94.58 198	65.06 255	67.12 283	88.57 240

miper_lstm_enhance		73.05 266	71.73 269	77.03 303	83.80 282	58.32 306	81.76 327	88.88 276	69.80 252	61.01 315	78.23 330	57.19 171	87.51 345	65.34 253	59.53 345	85.27 303

jajsoiax		73.05 266	71.51 271	77.67 294	77.46 354	54.83 335	88.81 262	90.04 228	69.13 261	62.85 308	83.51 266	31.16 362	92.75 265	70.83 194	69.80 260	85.43 299

LCM-MVSNet-Re		72.93 268	71.84 267	76.18 312	88.49 185	48.02 367	80.07 346	70.17 387	73.96 155	52.25 357	80.09 316	49.98 252	88.24 335	67.35 227	84.23 151	92.28 182

pm-mvs1		72.89 269	71.09 273	78.26 289	79.10 336	57.62 313	90.80 212	89.30 254	67.66 273	62.91 307	81.78 285	49.11 265	92.95 254	60.29 286	58.89 348	84.22 310

tpmvs		72.88 270	69.76 286	82.22 206	90.98 131	67.05 116	78.22 356	88.30 295	63.10 310	64.35 294	74.98 354	55.09 202	94.27 213	43.25 353	69.57 263	85.34 301

test0.0.03 1		72.76 271	72.71 257	72.88 335	80.25 320	47.99 368	91.22 198	89.45 248	71.51 225	62.51 311	87.66 215	53.83 215	85.06 358	50.16 322	67.84 281	85.58 294

UniMVSNet_ETH3D		72.74 272	70.53 279	79.36 276	78.62 344	56.64 324	85.01 302	89.20 258	63.77 301	64.84 287	84.44 257	34.05 350	91.86 292	63.94 261	70.89 259	89.57 228

mvs_tets		72.71 273	71.11 272	77.52 295	77.41 355	54.52 337	88.45 268	89.76 236	68.76 266	62.70 309	83.26 269	29.49 366	92.71 266	70.51 200	69.62 262	85.34 301

FMVSNet1		72.71 273	69.91 284	81.10 235	83.60 286	65.11 164	90.01 237	90.32 212	63.92 299	63.56 299	80.25 313	36.35 342	91.54 301	54.46 308	66.75 286	86.64 269

test_fmvs1_n		72.69 275	71.92 266	74.99 319	71.15 377	47.08 374	87.34 287	75.67 370	63.48 304	78.08 136	91.17 160	20.16 389	87.87 338	84.65 87	75.57 226	90.01 221

IterMVS		72.65 276	70.83 274	78.09 291	82.17 300	62.96 225	87.64 283	86.28 321	71.56 223	60.44 319	78.85 326	45.42 294	86.66 349	63.30 267	61.83 328	84.65 308
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 277	72.74 255	72.10 343	87.87 207	49.45 361	88.07 272	89.01 271	72.91 177	63.11 303	88.10 207	63.63 98	85.54 354	32.73 388	69.23 267	81.32 345

PatchMatch-RL		72.06 278	69.98 281	78.28 288	89.51 161	55.70 330	83.49 311	83.39 352	61.24 326	63.72 298	82.76 273	34.77 347	93.03 251	53.37 314	77.59 208	86.12 283

PVSNet_0	68.08 15	71.81 279	68.32 295	82.27 203	84.68 267	62.31 242	88.68 264	90.31 215	75.84 127	57.93 337	80.65 307	37.85 330	94.19 216	69.94 202	29.05 403	90.31 217

MIMVSNet		71.64 280	68.44 293	81.23 230	81.97 303	64.44 178	73.05 371	88.80 280	69.67 253	64.59 288	74.79 356	32.79 353	87.82 339	53.99 310	76.35 221	91.42 198

test_vis1_n		71.63 281	70.73 277	74.31 326	69.63 383	47.29 373	86.91 291	72.11 381	63.21 308	75.18 166	90.17 178	20.40 387	85.76 353	84.59 88	74.42 232	89.87 222

IterMVS-SCA-FT		71.55 282	69.97 282	76.32 310	81.48 306	60.67 276	87.64 283	85.99 326	66.17 285	59.50 324	78.88 325	45.53 292	83.65 366	62.58 273	61.93 327	84.63 309

v7n		71.31 283	68.65 290	79.28 277	76.40 359	60.77 270	86.71 294	89.45 248	64.17 298	58.77 331	78.24 329	44.59 299	93.54 242	57.76 296	61.75 330	83.52 318

anonymousdsp		71.14 284	69.37 288	76.45 309	72.95 372	54.71 336	84.19 306	88.88 276	61.92 322	62.15 312	79.77 319	38.14 326	91.44 306	68.90 215	67.45 282	83.21 324

F-COLMAP		70.66 285	68.44 293	77.32 300	86.37 239	55.91 328	88.00 274	86.32 320	56.94 352	57.28 341	88.07 209	33.58 351	92.49 276	51.02 318	68.37 274	83.55 316

WR-MVS_H		70.59 286	69.94 283	72.53 337	81.03 309	51.43 349	87.35 286	92.03 142	67.38 276	60.23 321	80.70 304	55.84 194	83.45 368	46.33 343	58.58 350	82.72 331

CP-MVSNet		70.50 287	69.91 284	72.26 340	80.71 313	51.00 353	87.23 288	90.30 216	67.84 271	59.64 323	82.69 274	50.23 251	82.30 376	51.28 317	59.28 346	83.46 320

RPMNet		70.42 288	65.68 308	84.63 139	83.15 291	67.96 92	70.25 377	90.45 206	46.83 383	69.97 230	65.10 383	56.48 187	95.30 177	35.79 378	73.13 241	90.64 213

testing3		70.38 289	70.83 274	69.03 355	85.82 250	43.93 386	90.72 215	90.56 205	68.06 270	60.24 320	86.82 231	64.83 82	84.12 360	26.33 396	64.10 310	79.04 366

tfpnnormal		70.10 290	67.36 299	78.32 287	83.45 288	60.97 266	88.85 261	92.77 112	64.85 294	60.83 317	78.53 327	43.52 303	93.48 244	31.73 391	61.70 332	80.52 354

TransMVSNet (Re)		70.07 291	67.66 297	77.31 301	80.62 316	59.13 299	91.78 172	84.94 336	65.97 286	60.08 322	80.44 309	50.78 245	91.87 291	48.84 329	45.46 378	80.94 349

CL-MVSNet_self_test		69.92 292	68.09 296	75.41 315	73.25 371	55.90 329	90.05 236	89.90 232	69.96 249	61.96 314	76.54 344	51.05 244	87.64 342	49.51 326	50.59 370	82.70 333

DP-MVS		69.90 293	66.48 301	80.14 255	95.36 28	62.93 226	89.56 245	76.11 368	50.27 373	57.69 339	85.23 247	39.68 315	95.73 154	33.35 383	71.05 258	81.78 343

PS-CasMVS		69.86 294	69.13 289	72.07 344	80.35 318	50.57 355	87.02 290	89.75 237	67.27 277	59.19 327	82.28 278	46.58 283	82.24 377	50.69 319	59.02 347	83.39 322

Syy-MVS		69.65 295	69.52 287	70.03 351	87.87 207	43.21 387	88.07 272	89.01 271	72.91 177	63.11 303	88.10 207	45.28 295	85.54 354	22.07 401	69.23 267	81.32 345

MSDG		69.54 296	65.73 307	80.96 240	85.11 263	63.71 202	84.19 306	83.28 353	56.95 351	54.50 348	84.03 260	31.50 359	96.03 144	42.87 357	69.13 269	83.14 326

PEN-MVS		69.46 297	68.56 291	72.17 342	79.27 331	49.71 359	86.90 292	89.24 256	67.24 280	59.08 328	82.51 277	47.23 279	83.54 367	48.42 331	57.12 351	83.25 323

LS3D		69.17 298	66.40 303	77.50 296	91.92 105	56.12 327	85.12 301	80.37 361	46.96 381	56.50 343	87.51 219	37.25 334	93.71 239	32.52 390	79.40 192	82.68 334

PatchT		69.11 299	65.37 312	80.32 249	82.07 302	63.68 205	67.96 387	87.62 308	50.86 371	69.37 234	65.18 382	57.09 172	88.53 332	41.59 362	66.60 287	88.74 237

KD-MVS_2432*1600		69.03 300	66.37 304	77.01 304	85.56 254	61.06 264	81.44 332	90.25 218	67.27 277	58.00 335	76.53 345	54.49 207	87.63 343	48.04 333	35.77 394	82.34 337

miper_refine_blended		69.03 300	66.37 304	77.01 304	85.56 254	61.06 264	81.44 332	90.25 218	67.27 277	58.00 335	76.53 345	54.49 207	87.63 343	48.04 333	35.77 394	82.34 337

mvsany_test1		68.77 302	68.56 291	69.39 353	73.57 370	45.88 381	80.93 337	60.88 401	59.65 337	71.56 211	90.26 176	43.22 304	75.05 388	74.26 166	62.70 319	87.25 262

ACMH	63.93 17	68.62 303	64.81 314	80.03 259	85.22 259	63.25 216	87.72 280	84.66 338	60.83 329	51.57 361	79.43 323	27.29 373	94.96 185	41.76 360	64.84 301	81.88 341
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 304	65.41 311	77.96 292	78.69 342	62.93 226	89.86 242	89.17 260	60.55 330	50.27 366	77.73 335	22.60 383	94.06 223	47.18 339	72.65 246	76.88 376

ADS-MVSNet		68.54 305	64.38 321	81.03 239	88.06 201	66.90 121	68.01 385	84.02 344	57.57 345	64.48 290	69.87 373	38.68 317	89.21 328	40.87 364	67.89 279	86.97 264

DTE-MVSNet		68.46 306	67.33 300	71.87 346	77.94 351	49.00 365	86.16 298	88.58 289	66.36 284	58.19 332	82.21 280	46.36 284	83.87 365	44.97 350	55.17 358	82.73 330

our_test_3		68.29 307	64.69 316	79.11 282	78.92 337	64.85 171	88.40 269	85.06 334	60.32 333	52.68 355	76.12 349	40.81 312	89.80 325	44.25 352	55.65 356	82.67 335

Patchmatch-RL test		68.17 308	64.49 319	79.19 278	71.22 376	53.93 339	70.07 379	71.54 385	69.22 258	56.79 342	62.89 387	56.58 184	88.61 329	69.53 206	52.61 365	95.03 85

XVG-ACMP-BASELINE		68.04 309	65.53 310	75.56 314	74.06 369	52.37 344	78.43 353	85.88 327	62.03 320	58.91 330	81.21 300	20.38 388	91.15 308	60.69 283	68.18 275	83.16 325

FMVSNet5		68.04 309	65.66 309	75.18 318	84.43 274	57.89 308	83.54 310	86.26 322	61.83 324	53.64 353	73.30 359	37.15 337	85.08 357	48.99 328	61.77 329	82.56 336

ppachtmachnet_test		67.72 311	63.70 323	79.77 269	78.92 337	66.04 141	88.68 264	82.90 355	60.11 335	55.45 345	75.96 350	39.19 316	90.55 310	39.53 368	52.55 366	82.71 332

ACMH+	65.35 16	67.65 312	64.55 317	76.96 306	84.59 270	57.10 319	88.08 271	80.79 359	58.59 343	53.00 354	81.09 302	26.63 375	92.95 254	46.51 341	61.69 333	80.82 350

pmmvs6		67.57 313	64.76 315	76.00 313	72.82 374	53.37 341	88.71 263	86.78 318	53.19 363	57.58 340	78.03 332	35.33 346	92.41 278	55.56 304	54.88 360	82.21 339

Anonymous20231206		67.53 314	65.78 306	72.79 336	74.95 365	47.59 370	88.23 270	87.32 310	61.75 325	58.07 334	77.29 338	37.79 331	87.29 347	42.91 355	63.71 314	83.48 319

Patchmtry		67.53 314	63.93 322	78.34 286	82.12 301	64.38 182	68.72 382	84.00 345	48.23 380	59.24 325	72.41 363	57.82 166	89.27 327	46.10 344	56.68 355	81.36 344

USDC		67.43 316	64.51 318	76.19 311	77.94 351	55.29 332	78.38 354	85.00 335	73.17 170	48.36 374	80.37 310	21.23 385	92.48 277	52.15 316	64.02 312	80.81 351

ADS-MVSNet2		66.90 317	63.44 325	77.26 302	88.06 201	60.70 275	68.01 385	75.56 372	57.57 345	64.48 290	69.87 373	38.68 317	84.10 361	40.87 364	67.89 279	86.97 264

CMPMVS	48.56 21	66.77 318	64.41 320	73.84 328	70.65 380	50.31 356	77.79 358	85.73 330	45.54 385	44.76 384	82.14 281	35.40 345	90.14 320	63.18 268	74.54 230	81.07 348
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 319	62.92 328	76.80 308	76.51 358	57.77 310	89.22 254	83.41 351	55.48 358	53.86 352	77.84 333	26.28 376	93.95 232	34.90 380	68.76 271	78.68 369

LTVRE_ROB	59.60 19	66.27 320	63.54 324	74.45 323	84.00 281	51.55 348	67.08 389	83.53 349	58.78 341	54.94 347	80.31 311	34.54 348	93.23 248	40.64 366	68.03 277	78.58 370
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		66.06 321	62.45 331	76.88 307	81.42 308	54.45 338	57.49 401	88.67 285	49.36 375	63.86 296	46.86 399	56.06 191	90.25 314	49.53 325	68.83 270	85.95 287

Patchmatch-test		65.86 322	60.94 337	80.62 246	83.75 283	58.83 301	58.91 400	75.26 374	44.50 388	50.95 365	77.09 341	58.81 157	87.90 337	35.13 379	64.03 311	95.12 80

UnsupCasMVSNet_eth		65.79 323	63.10 326	73.88 327	70.71 379	50.29 357	81.09 335	89.88 233	72.58 184	49.25 371	74.77 357	32.57 355	87.43 346	55.96 303	41.04 385	83.90 313

test_fmvs2		65.78 324	64.84 313	68.60 357	66.54 389	41.71 389	83.27 315	69.81 388	54.38 360	67.91 256	84.54 256	15.35 394	81.22 381	75.65 153	66.16 289	82.88 327

dmvs_testset		65.55 325	66.45 302	62.86 369	79.87 324	22.35 415	76.55 361	71.74 383	77.42 110	55.85 344	87.77 214	51.39 240	80.69 382	31.51 394	65.92 291	85.55 296

pmmvs-eth3d		65.53 326	62.32 332	75.19 317	69.39 384	59.59 290	82.80 323	83.43 350	62.52 315	51.30 363	72.49 361	32.86 352	87.16 348	55.32 305	50.73 369	78.83 368

mamv4		65.18 327	67.43 298	58.44 373	77.88 353	49.36 364	69.40 381	70.99 386	48.31 379	57.78 338	85.53 245	59.01 155	51.88 411	73.67 168	64.32 307	74.07 381

SixPastTwentyTwo		64.92 328	61.78 335	74.34 325	78.74 341	49.76 358	83.42 314	79.51 364	62.86 311	50.27 366	77.35 336	30.92 364	90.49 312	45.89 345	47.06 375	82.78 328

OurMVSNet-221017-0		64.68 329	62.17 333	72.21 341	76.08 362	47.35 371	80.67 338	81.02 358	56.19 355	51.60 360	79.66 321	27.05 374	88.56 331	53.60 313	53.63 363	80.71 352

test_0402		64.54 330	61.09 336	74.92 320	84.10 280	60.75 272	87.95 275	79.71 363	52.03 365	52.41 356	77.20 339	32.21 357	91.64 297	23.14 399	61.03 336	72.36 387

testgi		64.48 331	62.87 329	69.31 354	71.24 375	40.62 392	85.49 299	79.92 362	65.36 291	54.18 350	83.49 267	23.74 380	84.55 359	41.60 361	60.79 339	82.77 329

RPSCF		64.24 332	61.98 334	71.01 349	76.10 361	45.00 382	75.83 366	75.94 369	46.94 382	58.96 329	84.59 254	31.40 360	82.00 378	47.76 337	60.33 344	86.04 284

EU-MVSNet		64.01 333	63.01 327	67.02 363	74.40 368	38.86 398	83.27 315	86.19 324	45.11 386	54.27 349	81.15 301	36.91 340	80.01 384	48.79 330	57.02 352	82.19 340

test20.03		63.83 334	62.65 330	67.38 362	70.58 381	39.94 394	86.57 295	84.17 342	63.29 306	51.86 359	77.30 337	37.09 338	82.47 374	38.87 372	54.13 362	79.73 360

MDA-MVSNet_test_wron		63.78 335	60.16 339	74.64 321	78.15 349	60.41 279	83.49 311	84.03 343	56.17 357	39.17 394	71.59 369	37.22 335	83.24 371	42.87 357	48.73 372	80.26 357

YYNet1		63.76 336	60.14 340	74.62 322	78.06 350	60.19 284	83.46 313	83.99 347	56.18 356	39.25 393	71.56 370	37.18 336	83.34 369	42.90 356	48.70 373	80.32 356

K. test v3		63.09 337	59.61 342	73.53 330	76.26 360	49.38 363	83.27 315	77.15 367	64.35 297	47.77 376	72.32 365	28.73 368	87.79 340	49.93 324	36.69 392	83.41 321

COLMAP_ROB	57.96 20	62.98 338	59.65 341	72.98 334	81.44 307	53.00 343	83.75 309	75.53 373	48.34 378	48.81 373	81.40 294	24.14 378	90.30 313	32.95 385	60.52 341	75.65 379
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 339	59.08 343	71.10 348	67.19 387	48.72 366	83.91 308	85.23 333	50.38 372	47.84 375	71.22 372	20.74 386	85.51 356	46.47 342	58.75 349	79.06 365

AllTest		61.66 340	58.06 345	72.46 338	79.57 326	51.42 350	80.17 344	68.61 390	51.25 369	45.88 378	81.23 296	19.86 390	86.58 350	38.98 370	57.01 353	79.39 362

UnsupCasMVSNet_bld		61.60 341	57.71 346	73.29 332	68.73 385	51.64 347	78.61 352	89.05 270	57.20 350	46.11 377	61.96 390	28.70 369	88.60 330	50.08 323	38.90 390	79.63 361

MDA-MVSNet-bldmvs		61.54 342	57.70 347	73.05 333	79.53 328	57.00 323	83.08 319	81.23 357	57.57 345	34.91 398	72.45 362	32.79 353	86.26 352	35.81 377	41.95 383	75.89 378

mvs5depth		61.03 343	57.65 348	71.18 347	67.16 388	47.04 376	72.74 372	77.49 365	57.47 348	60.52 318	72.53 360	22.84 382	88.38 333	49.15 327	38.94 389	78.11 373

KD-MVS_self_test		60.87 344	58.60 344	67.68 360	66.13 390	39.93 395	75.63 368	84.70 337	57.32 349	49.57 369	68.45 377	29.55 365	82.87 372	48.09 332	47.94 374	80.25 358

kuosan		60.86 345	60.24 338	62.71 370	81.57 305	46.43 378	75.70 367	85.88 327	57.98 344	48.95 372	69.53 375	58.42 159	76.53 386	28.25 395	35.87 393	65.15 394

TinyColmap		60.32 346	56.42 353	72.00 345	78.78 340	53.18 342	78.36 355	75.64 371	52.30 364	41.59 392	75.82 352	14.76 397	88.35 334	35.84 376	54.71 361	74.46 380

MVS-HIRNet		60.25 347	55.55 354	74.35 324	84.37 275	56.57 325	71.64 375	74.11 376	34.44 397	45.54 382	42.24 405	31.11 363	89.81 323	40.36 367	76.10 223	76.67 377

MIMVSNet1		60.16 348	57.33 349	68.67 356	69.71 382	44.13 384	78.92 351	84.21 341	55.05 359	44.63 385	71.85 367	23.91 379	81.54 380	32.63 389	55.03 359	80.35 355

PM-MVS		59.40 349	56.59 351	67.84 358	63.63 393	41.86 388	76.76 360	63.22 398	59.01 340	51.07 364	72.27 366	11.72 401	83.25 370	61.34 279	50.28 371	78.39 371

new-patchmatchnet		59.30 350	56.48 352	67.79 359	65.86 391	44.19 383	82.47 324	81.77 356	59.94 336	43.65 388	66.20 381	27.67 372	81.68 379	39.34 369	41.40 384	77.50 375

test_vis1_rt		59.09 351	57.31 350	64.43 366	68.44 386	46.02 380	83.05 321	48.63 410	51.96 366	49.57 369	63.86 386	16.30 392	80.20 383	71.21 192	62.79 318	67.07 393

test_fmvs3		56.82 352	54.86 356	62.69 371	53.59 404	35.47 401	75.87 365	65.64 395	43.91 389	55.10 346	71.43 371	6.91 409	74.40 391	68.64 217	52.63 364	78.20 372

DSMNet-mixed		56.78 353	54.44 357	63.79 367	63.21 394	29.44 410	64.43 392	64.10 397	42.12 394	51.32 362	71.60 368	31.76 358	75.04 389	36.23 375	65.20 298	86.87 267

pmmvs3		55.51 354	51.50 360	67.53 361	57.90 402	50.93 354	80.37 340	73.66 377	40.63 395	44.15 387	64.75 384	16.30 392	78.97 385	44.77 351	40.98 387	72.69 385

TDRefinement		55.28 355	51.58 359	66.39 364	59.53 401	46.15 379	76.23 363	72.80 378	44.60 387	42.49 390	76.28 348	15.29 395	82.39 375	33.20 384	43.75 380	70.62 389

dongtai		55.18 356	55.46 355	54.34 381	76.03 363	36.88 399	76.07 364	84.61 339	51.28 368	43.41 389	64.61 385	56.56 185	67.81 399	18.09 404	28.50 404	58.32 397

LF4IMVS		54.01 357	52.12 358	59.69 372	62.41 396	39.91 396	68.59 383	68.28 392	42.96 392	44.55 386	75.18 353	14.09 399	68.39 398	41.36 363	51.68 367	70.78 388

m2depth		53.34 358	49.96 361	63.45 368	62.07 398	40.04 393	72.06 373	65.64 395	42.54 393	51.88 358	77.79 334	13.94 400	76.48 387	32.93 386	30.82 402	73.84 382

MVStest1		51.35 359	46.89 363	64.74 365	65.06 392	51.10 352	67.33 388	72.58 379	30.20 401	35.30 396	74.82 355	27.70 371	69.89 396	24.44 398	24.57 405	73.22 383

N_pmnet		50.55 360	49.11 362	54.88 379	77.17 356	4.02 423	84.36 305	2.00 421	48.59 376	45.86 380	68.82 376	32.22 356	82.80 373	31.58 392	51.38 368	77.81 374

new_pmnet		49.31 361	46.44 364	57.93 374	62.84 395	40.74 391	68.47 384	62.96 399	36.48 396	35.09 397	57.81 394	14.97 396	72.18 393	32.86 387	46.44 376	60.88 396

mvsany_test3		48.86 362	46.35 365	56.41 375	46.00 410	31.67 406	62.26 394	47.25 411	43.71 390	45.54 382	68.15 378	10.84 402	64.44 407	57.95 295	35.44 396	73.13 384

test_f		46.58 363	43.45 367	55.96 376	45.18 411	32.05 405	61.18 395	49.49 409	33.39 398	42.05 391	62.48 389	7.00 408	65.56 403	47.08 340	43.21 382	70.27 390

WB-MVS		46.23 364	44.94 366	50.11 384	62.13 397	21.23 417	76.48 362	55.49 403	45.89 384	35.78 395	61.44 392	35.54 344	72.83 392	9.96 411	21.75 406	56.27 399

FPMVS		45.64 365	43.10 369	53.23 382	51.42 407	36.46 400	64.97 391	71.91 382	29.13 402	27.53 402	61.55 391	9.83 404	65.01 405	16.00 408	55.58 357	58.22 398

SSC-MVS		44.51 366	43.35 368	47.99 388	61.01 400	18.90 419	74.12 370	54.36 404	43.42 391	34.10 399	60.02 393	34.42 349	70.39 395	9.14 413	19.57 407	54.68 400

EGC-MVSNET		42.35 367	38.09 370	55.11 378	74.57 366	46.62 377	71.63 376	55.77 402	0.04 416	0.24 417	62.70 388	14.24 398	74.91 390	17.59 405	46.06 377	43.80 402

LCM-MVSNet		40.54 368	35.79 373	54.76 380	36.92 417	30.81 407	51.41 404	69.02 389	22.07 404	24.63 404	45.37 401	4.56 413	65.81 402	33.67 382	34.50 397	67.67 391

APD_test1		40.50 369	37.31 372	50.09 385	51.88 405	35.27 402	59.45 399	52.59 406	21.64 405	26.12 403	57.80 395	4.56 413	66.56 401	22.64 400	39.09 388	48.43 401

test_vis3_rt		40.46 370	37.79 371	48.47 387	44.49 412	33.35 404	66.56 390	32.84 418	32.39 399	29.65 400	39.13 408	3.91 416	68.65 397	50.17 321	40.99 386	43.40 403

ANet_high		40.27 371	35.20 374	55.47 377	34.74 418	34.47 403	63.84 393	71.56 384	48.42 377	18.80 407	41.08 406	9.52 405	64.45 406	20.18 402	8.66 414	67.49 392

test_method		38.59 372	35.16 375	48.89 386	54.33 403	21.35 416	45.32 407	53.71 405	7.41 413	28.74 401	51.62 397	8.70 406	52.87 410	33.73 381	32.89 398	72.47 386

PMMVS2		37.93 373	33.61 376	50.92 383	46.31 409	24.76 413	60.55 398	50.05 407	28.94 403	20.93 405	47.59 398	4.41 415	65.13 404	25.14 397	18.55 409	62.87 395

Gipuma		34.91 374	31.44 377	45.30 389	70.99 378	39.64 397	19.85 411	72.56 380	20.10 407	16.16 411	21.47 412	5.08 412	71.16 394	13.07 409	43.70 381	25.08 409
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 375	29.47 378	42.67 391	41.89 414	30.81 407	52.07 402	43.45 412	15.45 408	18.52 408	44.82 402	2.12 417	58.38 408	16.05 406	30.87 400	38.83 404

APD_test2		32.77 375	29.47 378	42.67 391	41.89 414	30.81 407	52.07 402	43.45 412	15.45 408	18.52 408	44.82 402	2.12 417	58.38 408	16.05 406	30.87 400	38.83 404

PMVS	26.43 22	31.84 377	28.16 380	42.89 390	25.87 420	27.58 411	50.92 405	49.78 408	21.37 406	14.17 412	40.81 407	2.01 419	66.62 400	9.61 412	38.88 391	34.49 408
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 378	24.00 382	26.45 395	43.74 413	18.44 420	60.86 396	39.66 414	15.11 410	9.53 414	22.10 411	6.52 410	46.94 413	8.31 414	10.14 411	13.98 411

MVE	24.84 23	24.35 379	19.77 385	38.09 393	34.56 419	26.92 412	26.57 409	38.87 416	11.73 412	11.37 413	27.44 409	1.37 420	50.42 412	11.41 410	14.60 410	36.93 406
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 380	23.20 384	25.46 396	41.52 416	16.90 421	60.56 397	38.79 417	14.62 411	8.99 415	20.24 414	7.35 407	45.82 414	7.25 415	9.46 412	13.64 412

tmp_tt		22.26 381	23.75 383	17.80 397	5.23 421	12.06 422	35.26 408	39.48 415	2.82 415	18.94 406	44.20 404	22.23 384	24.64 416	36.30 374	9.31 413	16.69 410

cdsmvs_eth3d_5k		19.86 382	26.47 381	0.00 401	0.00 424	0.00 426	0.00 412	93.45 85	0.00 419	0.00 420	95.27 56	49.56 257	0.00 420	0.00 419	0.00 417	0.00 416

wuyk23d		11.30 383	10.95 386	12.33 398	48.05 408	19.89 418	25.89 410	1.92 422	3.58 414	3.12 416	1.37 416	0.64 421	15.77 417	6.23 416	7.77 415	1.35 413

ab-mvs-re		7.91 384	10.55 387	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	94.95 66	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

testmvs		7.23 385	9.62 388	0.06 400	0.04 422	0.02 425	84.98 303	0.02 423	0.03 417	0.18 418	1.21 417	0.01 423	0.02 418	0.14 417	0.01 416	0.13 415

test123		6.92 386	9.21 389	0.08 399	0.03 423	0.05 424	81.65 330	0.01 424	0.02 418	0.14 419	0.85 418	0.03 422	0.02 418	0.12 418	0.00 417	0.16 414

pcd_1.5k_mvsjas		4.46 387	5.95 390	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	53.55 219	0.00 420	0.00 419	0.00 417	0.00 416

monomultidepth		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

test_blank		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

uanet_test		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

DCPMVS		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

sosnet-low-res		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

sosnet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

uncertanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

Regformer		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

uanet		0.00 388	0.00 391	0.00 401	0.00 424	0.00 426	0.00 412	0.00 425	0.00 419	0.00 420	0.00 419	0.00 424	0.00 420	0.00 419	0.00 417	0.00 416

WAC-MVS						49.45 361								31.56 393

FOURS1					93.95 46	61.77 251	93.96 70	91.92 146	62.14 319	86.57 46

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 26					99.07 13	92.01 25	94.77 26	96.51 24

PC_three_1452									80.91 47	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 12		95.05 26					99.07 13	92.01 25	94.77 26	96.51 24

test_one_0601					96.32 18	69.74 49		94.18 57	71.42 227	90.67 19	96.85 16	74.45 19

eth-test2					0.00 424

eth-test					0.00 424

ZD-MVS					96.63 9	65.50 156		93.50 83	70.74 241	85.26 62	95.19 62	64.92 81	97.29 76	87.51 57	93.01 56

RE-MVS-def			80.48 143		92.02 99	58.56 304	90.90 207	90.45 206	62.76 312	78.89 125	94.46 80	49.30 260		78.77 136	86.77 127	92.28 182

IU-MVS					96.46 11	69.91 42		95.18 20	80.75 48	95.28 1				92.34 22	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				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 9	97.21 4	74.58 17	99.11 6	92.34 22	95.36 14	96.59 19

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

9.14			87.63 28		93.86 48		94.41 52	94.18 57	72.76 181	86.21 48	96.51 24	66.64 62	97.88 44	90.08 39	94.04 39

save fliter					93.84 49	67.89 94	95.05 39	92.66 118	78.19 93

test_0728_THIRD									72.48 186	90.55 20	96.93 11	76.24 11	99.08 11	91.53 30	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 33	96.64 10	94.37 52					99.15 2	91.91 28	94.90 22	96.51 24

test0726					96.40 15	69.99 38	96.76 8	94.33 54	71.92 202	91.89 11	97.11 6	73.77 22

GSMVS																94.68 99

test_part2					96.29 19	68.16 88				90.78 17

sam_mvs1												57.85 165				94.68 99

sam_mvs												54.91 204

ambc				69.61 352	61.38 399	41.35 390	49.07 406	85.86 329		50.18 368	66.40 380	10.16 403	88.14 336	45.73 346	44.20 379	79.32 364

MTGPA								92.23 131

test_post1							78.95 350				20.70 413	53.05 224	91.50 305	60.43 284

test_post											23.01 410	56.49 186	92.67 269

patchmatchnet-post											67.62 379	57.62 168	90.25 314

GG-mvs-BLEND				86.53 73	91.91 106	69.67 52	75.02 369	94.75 33		78.67 132	90.85 164	77.91 7	94.56 202	72.25 182	93.74 45	95.36 65

MTMP							93.77 84	32.52 419

gm-plane-assit					88.42 189	67.04 117			78.62 89		91.83 147		97.37 70	76.57 148

test9_res														89.41 40	94.96 19	95.29 70

TEST9					94.18 41	67.28 109	94.16 59	93.51 81	71.75 213	85.52 57	95.33 51	68.01 52	97.27 80

test_8					94.19 40	67.19 111	94.15 61	93.42 88	71.87 207	85.38 60	95.35 50	68.19 50	96.95 103

agg_prior2														86.41 70	94.75 30	95.33 66

agg_prior					94.16 43	66.97 119		93.31 91		84.49 68			96.75 113

TestCases				72.46 338	79.57 326	51.42 350		68.61 390	51.25 369	45.88 378	81.23 296	19.86 390	86.58 350	38.98 370	57.01 353	79.39 362

test_prior4						67.18 113	93.92 73

test_prior2							95.10 38		75.40 134	85.25 63	95.61 45	67.94 53		87.47 59	94.77 26

test_prior				86.42 76	94.71 35	67.35 108		93.10 101					96.84 110			95.05 83

旧先验2							92.00 161		59.37 339	87.54 39			93.47 245	75.39 155

新几何2							91.41 182

新几何1				84.73 132	92.32 91	64.28 187		91.46 172	59.56 338	79.77 114	92.90 120	56.95 178	96.57 118	63.40 264	92.91 58	93.34 149

旧先验1					91.94 103	60.74 273		91.50 170			94.36 84	65.23 76			91.84 71	94.55 106

无先验							92.71 126	92.61 122	62.03 320				97.01 93	66.63 235		93.97 131

原ACMM2							92.01 158

原ACMM1				84.42 146	93.21 67	64.27 188		93.40 90	65.39 290	79.51 117	92.50 128	58.11 164	96.69 114	65.27 254	93.96 40	92.32 180

test222					89.77 154	61.60 256	89.55 246	89.42 250	56.83 353	77.28 145	92.43 132	52.76 227			91.14 85	93.09 158

testdata2													96.09 138	61.26 280

segment_acmp												65.94 69

testdata				81.34 228	89.02 175	57.72 311		89.84 234	58.65 342	85.32 61	94.09 96	57.03 173	93.28 247	69.34 208	90.56 91	93.03 161

testdata1							89.21 255		77.55 106

test12				87.09 52	94.60 36	68.86 67		92.91 108		82.67 84		65.44 74	97.55 62		93.69 48	94.84 92

plane_prior7					86.94 229	61.51 257

plane_prior6					87.23 221	62.32 241						50.66 246

plane_prior5								91.31 176					95.55 167	76.74 146	78.53 202	88.39 244

plane_prior4											89.14 193

plane_prior3						61.95 249			79.09 79	72.53 195

plane_prior2							93.13 110		78.81 86

plane_prior1					87.15 223

plane_prior						62.42 237	93.85 77		79.38 71						78.80 199

n2								0.00 425

nn								0.00 425

door-mid								66.01 394

lessismore_v0				73.72 329	72.93 373	47.83 369		61.72 400		45.86 380	73.76 358	28.63 370	89.81 323	47.75 338	31.37 399	83.53 317

LGP-MVS_train				79.56 274	84.31 276	59.37 294		89.73 240	69.49 254	64.86 285	88.42 198	38.65 319	94.30 211	72.56 179	72.76 244	85.01 304

test11								93.01 104

door								66.57 393

HQP5-MVS						63.66 206

HQP-NCC					87.54 214		94.06 63		79.80 62	74.18 174

ACMP_Plane					87.54 214		94.06 63		79.80 62	74.18 174

BP-MVS														77.63 143

HQP4-MVS										74.18 174			95.61 162			88.63 238

HQP3-MVS								91.70 162							78.90 197

HQP2-MVS												51.63 238

NP-MVS					87.41 217	63.04 222					90.30 174

MDTV_nov1_ep13_2view						59.90 287	80.13 345		67.65 274	72.79 189		54.33 212		59.83 288		92.58 173

MDTV_nov1_ep13			72.61 258		89.06 174	68.48 76	80.33 341	90.11 224	71.84 209	71.81 207	75.92 351	53.01 225	93.92 233	48.04 333	73.38 239

ACMMP++_ref															71.63 252

ACMMP++															69.72 261

Test By Simon												54.21 213

ITE_SJBPF				70.43 350	74.44 367	47.06 375		77.32 366	60.16 334	54.04 351	83.53 265	23.30 381	84.01 363	43.07 354	61.58 334	80.21 359

DeepMVS_CX				34.71 394	51.45 406	24.73 414		28.48 420	31.46 400	17.49 410	52.75 396	5.80 411	42.60 415	18.18 403	19.42 408	36.81 407

High-resolution multi-view benchmark