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 8	97.13 2	95.58 11	89.33 1	85.77 51	96.26 30	72.84 26	99.38 1	92.64 19	95.93 9	97.08 9

MM		90.87 2	91.52 2	88.92 13	92.12 92	71.10 22	97.02 3	96.04 6	88.70 2	91.57 13	96.19 33	70.12 37	98.91 17	96.83 1	95.06 16	96.76 12

DPM-MVS		90.70 3	90.52 8	91.24 1	89.68 147	76.68 2	97.29 1	95.35 14	82.87 20	91.58 12	97.22 3	79.93 5	99.10 9	83.12 92	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 14	97.31 4	69.91 37	93.96 70	94.37 48	72.48 177	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 81	92.83 72	64.03 185	93.06 107	94.33 50	82.19 28	93.65 3	96.15 35	85.89 1	97.19 82	91.02 33	97.75 1	96.43 26
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 19	96.76 8	70.65 26	96.47 13	94.83 27	84.83 11	89.07 31	96.80 19	70.86 34	99.06 15	92.64 19	95.71 10	96.12 35

DELS-MVS		90.05 7	90.09 11	89.94 4	93.14 66	73.88 7	97.01 4	94.40 46	88.32 3	85.71 52	94.91 68	74.11 19	98.91 17	87.26 59	95.94 8	97.03 10
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MVS_0304		90.01 8	90.50 9	88.53 20	90.14 138	70.94 23	96.47 13	95.72 10	87.33 4	89.60 28	96.26 30	68.44 42	98.74 24	95.82 4	94.72 30	95.90 42

SED-MVS		89.94 9	90.36 10	88.70 16	96.45 12	69.38 48	96.89 5	94.44 42	71.65 207	92.11 6	97.21 4	76.79 9	99.11 6	92.34 21	95.36 13	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 125	93.00 69	58.16 298	96.72 8	94.41 44	86.50 8	90.25 21	97.83 1	75.46 14	98.67 25	92.78 18	95.49 12	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 84	96.04 24	63.70 195	95.04 40	95.19 17	86.74 7	91.53 14	95.15 62	73.86 20	97.58 59	93.38 14	92.00 67	96.28 32

CANet		89.61 12	89.99 12	88.46 21	94.39 39	69.71 44	96.53 12	93.78 62	86.89 6	89.68 27	95.78 40	65.94 63	99.10 9	92.99 16	93.91 40	96.58 18

DVP-MVS		89.41 13	89.73 14	88.45 22	96.40 15	69.99 33	96.64 9	94.52 38	71.92 193	90.55 19	96.93 11	73.77 21	99.08 11	91.91 27	94.90 21	96.29 30
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 30	95.10 30	68.23 79	95.24 33	94.49 40	82.43 25	88.90 32	96.35 27	71.89 33	98.63 26	88.76 47	96.40 6	96.06 36

NCCC		89.07 15	89.46 15	87.91 25	96.60 10	69.05 57	96.38 15	94.64 35	84.42 12	86.74 43	96.20 32	66.56 59	98.76 23	89.03 46	94.56 32	95.92 41

DPE-MVS		88.77 16	89.21 16	87.45 37	96.26 20	67.56 95	94.17 57	94.15 55	68.77 257	90.74 17	97.27 2	76.09 12	98.49 29	90.58 37	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		88.14 17	88.29 21	87.67 29	93.21 63	68.72 65	93.85 77	94.03 58	74.18 140	91.74 11	96.67 21	65.61 67	98.42 33	89.24 43	96.08 7	95.88 43
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 17	87.61 28	89.71 6	92.06 93	76.72 1	95.75 20	93.26 86	83.86 14	89.55 29	96.06 36	53.55 208	97.89 43	91.10 31	93.31 51	94.54 98

TSAR-MVS + MP.		88.11 19	88.64 17	86.54 64	91.73 105	68.04 83	90.36 220	93.55 75	82.89 19	91.29 15	92.89 119	72.27 30	96.03 135	87.99 50	94.77 25	95.54 52
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 20	88.37 20	86.70 57	93.51 56	65.32 150	95.15 36	93.84 61	78.17 87	85.93 50	94.80 71	75.80 13	98.21 34	89.38 40	88.78 101	96.59 16

DeepC-MVS_fast	79.48 2	87.95 21	88.00 24	87.79 28	95.86 27	68.32 73	95.74 21	94.11 56	83.82 15	83.49 73	96.19 33	64.53 81	98.44 31	83.42 91	94.88 24	96.61 15
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 22	87.38 32	89.55 11	91.41 116	76.43 3	95.74 21	93.12 94	83.53 17	89.55 29	95.95 38	53.45 212	97.68 50	91.07 32	92.62 58	94.54 98

EPNet		87.84 23	88.38 19	86.23 74	93.30 60	66.05 132	95.26 32	94.84 26	87.09 5	88.06 34	94.53 77	66.79 56	97.34 73	83.89 88	91.68 72	95.29 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 24	87.77 26	87.63 34	89.24 161	71.18 19	96.57 11	92.90 102	82.70 23	87.13 39	95.27 56	64.99 72	95.80 140	89.34 41	91.80 70	95.93 40

test_fmvsm_n_1920		87.69 25	88.50 18	85.27 105	87.05 217	63.55 202	93.69 87	91.08 179	84.18 13	90.17 23	97.04 8	67.58 51	97.99 39	95.72 5	90.03 92	94.26 106

APDe-MVS		87.54 26	87.84 25	86.65 58	96.07 23	66.30 128	94.84 45	93.78 62	69.35 248	88.39 33	96.34 28	67.74 50	97.66 54	90.62 36	93.44 49	96.01 39
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 27	88.19 22	85.39 99	86.95 218	64.37 175	94.30 54	88.45 280	80.51 48	92.70 4	96.86 15	69.98 38	97.15 86	95.83 3	88.08 108	94.65 92

SD-MVS		87.49 27	87.49 30	87.50 36	93.60 53	68.82 63	93.90 74	92.63 113	76.86 106	87.90 35	95.76 41	66.17 60	97.63 56	89.06 45	91.48 76	96.05 37
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 29	88.15 23	85.30 103	87.10 215	64.19 182	94.41 52	88.14 289	80.24 53	92.54 5	96.97 10	69.52 40	97.17 83	95.89 2	88.51 104	94.56 95

dcpmvs_2		87.37 30	87.55 29	86.85 50	95.04 32	68.20 80	90.36 220	90.66 191	79.37 66	81.20 89	93.67 103	74.73 15	96.55 118	90.88 34	92.00 67	95.82 44

alignmvs		87.28 31	86.97 36	88.24 24	91.30 117	71.14 21	95.61 25	93.56 74	79.30 67	87.07 41	95.25 58	68.43 43	96.93 105	87.87 51	84.33 142	96.65 14

train_agg		87.21 32	87.42 31	86.60 60	94.18 41	67.28 102	94.16 58	93.51 76	71.87 198	85.52 54	95.33 51	68.19 45	97.27 80	89.09 44	94.90 21	95.25 69

MG-MVS		87.11 33	86.27 42	89.62 7	97.79 1	76.27 4	94.96 43	94.49 40	78.74 82	83.87 72	92.94 117	64.34 82	96.94 103	75.19 148	94.09 36	95.66 47

SF-MVS		87.03 34	87.09 34	86.84 51	92.70 78	67.45 100	93.64 89	93.76 65	70.78 231	86.25 45	96.44 26	66.98 54	97.79 47	88.68 48	94.56 32	95.28 65

CSCG		86.87 35	86.26 43	88.72 15	95.05 31	70.79 25	93.83 82	95.33 15	68.48 261	77.63 132	94.35 86	73.04 24	98.45 30	84.92 79	93.71 45	96.92 11

canonicalmvs		86.85 36	86.25 44	88.66 18	91.80 104	71.92 14	93.54 94	91.71 150	80.26 52	87.55 37	95.25 58	63.59 95	96.93 105	88.18 49	84.34 141	97.11 8

PHI-MVS		86.83 37	86.85 40	86.78 55	93.47 57	65.55 146	95.39 30	95.10 20	71.77 203	85.69 53	96.52 23	62.07 112	98.77 22	86.06 70	95.60 11	96.03 38

SteuartSystems-ACMMP		86.82 38	86.90 38	86.58 62	90.42 132	66.38 125	96.09 17	93.87 60	77.73 94	84.01 71	95.66 43	63.39 97	97.94 40	87.40 57	93.55 48	95.42 53
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 39	86.86 39	86.31 73	93.76 49	67.53 97	96.33 16	93.61 72	82.34 27	81.00 94	93.08 113	63.19 101	97.29 76	87.08 61	91.38 78	94.13 113

test_fmvsmconf_n		86.58 40	87.17 33	84.82 118	85.28 248	62.55 226	94.26 56	89.78 224	83.81 16	87.78 36	96.33 29	65.33 69	96.98 98	94.40 11	87.55 113	94.95 78

jason		86.40 41	86.17 45	87.11 44	86.16 233	70.54 28	95.71 24	92.19 128	82.00 30	84.58 64	94.34 87	61.86 114	95.53 160	87.76 52	90.89 84	95.27 66
jason: jason.
fmvsm_s_conf0.5_n		86.39 42	86.91 37	84.82 118	87.36 210	63.54 203	94.74 47	90.02 218	82.52 24	90.14 24	96.92 13	62.93 105	97.84 46	95.28 8	82.26 156	93.07 149

WTY-MVS		86.32 43	85.81 52	87.85 26	92.82 74	69.37 50	95.20 34	95.25 16	82.71 22	81.91 84	94.73 72	67.93 49	97.63 56	79.55 117	82.25 157	96.54 19

MSLP-MVS++		86.27 44	85.91 51	87.35 39	92.01 96	68.97 60	95.04 40	92.70 107	79.04 76	81.50 87	96.50 25	58.98 147	96.78 110	83.49 90	93.93 39	96.29 30

VNet		86.20 45	85.65 55	87.84 27	93.92 46	69.99 33	95.73 23	95.94 7	78.43 84	86.00 49	93.07 114	58.22 152	97.00 94	85.22 74	84.33 142	96.52 20

MVS_111021_HR		86.19 46	85.80 53	87.37 38	93.17 65	69.79 41	93.99 69	93.76 65	79.08 74	78.88 120	93.99 97	62.25 111	98.15 36	85.93 71	91.15 82	94.15 112

CS-MVS-test		86.14 47	87.01 35	83.52 163	92.63 81	59.36 286	95.49 27	91.92 137	80.09 54	85.46 56	95.53 47	61.82 117	95.77 143	86.77 65	93.37 50	95.41 54

ACMMP_NAP		86.05 48	85.80 53	86.80 54	91.58 109	67.53 97	91.79 164	93.49 79	74.93 131	84.61 63	95.30 53	59.42 141	97.92 41	86.13 68	94.92 19	94.94 79

ETV-MVS		86.01 49	86.11 46	85.70 91	90.21 137	67.02 111	93.43 99	91.92 137	81.21 42	84.13 70	94.07 96	60.93 125	95.63 151	89.28 42	89.81 93	94.46 104

APD-MVS		85.93 50	85.99 49	85.76 88	95.98 26	65.21 153	93.59 92	92.58 115	66.54 275	86.17 47	95.88 39	63.83 88	97.00 94	86.39 67	92.94 55	95.06 73
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 51	85.46 56	87.18 42	88.20 189	72.42 13	92.41 137	92.77 105	82.11 29	80.34 100	93.07 114	68.27 44	95.02 173	78.39 130	93.59 47	94.09 115

CS-MVS		85.80 52	86.65 41	83.27 171	92.00 97	58.92 291	95.31 31	91.86 142	79.97 55	84.82 62	95.40 49	62.26 110	95.51 161	86.11 69	92.08 66	95.37 57

fmvsm_s_conf0.5_n_a		85.75 53	86.09 47	84.72 125	85.73 242	63.58 200	93.79 83	89.32 242	81.42 39	90.21 22	96.91 14	62.41 109	97.67 51	94.48 10	80.56 173	92.90 155

test_fmvsmconf0.1_n		85.71 54	86.08 48	84.62 132	80.83 301	62.33 230	93.84 80	88.81 267	83.50 18	87.00 42	96.01 37	63.36 98	96.93 105	94.04 12	87.29 116	94.61 94

CDPH-MVS		85.71 54	85.46 56	86.46 66	94.75 34	67.19 104	93.89 75	92.83 104	70.90 227	83.09 76	95.28 54	63.62 93	97.36 71	80.63 111	94.18 35	94.84 83

casdiffmvs_mvg		85.66 56	85.18 59	87.09 45	88.22 188	69.35 51	93.74 86	91.89 140	81.47 35	80.10 102	91.45 148	64.80 77	96.35 121	87.23 60	87.69 111	95.58 50
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 57	85.93 50	84.68 128	82.95 285	63.48 205	94.03 68	89.46 236	81.69 33	89.86 25	96.74 20	61.85 115	97.75 49	94.74 9	82.01 160	92.81 157

DeepC-MVS	77.85 3	85.52 58	85.24 58	86.37 70	88.80 171	66.64 119	92.15 144	93.68 70	81.07 43	76.91 142	93.64 104	62.59 107	98.44 31	85.50 72	92.84 57	94.03 119
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 59	84.87 65	86.84 51	88.25 186	69.07 56	93.04 109	91.76 147	81.27 41	80.84 96	92.07 138	64.23 83	96.06 133	84.98 78	87.43 115	95.39 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
ZNCC-MVS		85.33 60	85.08 61	86.06 76	93.09 68	65.65 142	93.89 75	93.41 83	73.75 151	79.94 104	94.68 74	60.61 128	98.03 38	82.63 95	93.72 44	94.52 100

MP-MVS-pluss		85.24 61	85.13 60	85.56 94	91.42 114	65.59 144	91.54 174	92.51 117	74.56 134	80.62 97	95.64 44	59.15 145	97.00 94	86.94 63	93.80 41	94.07 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 62	84.69 67	86.63 59	92.91 71	69.91 37	92.61 128	95.80 9	80.31 51	80.38 99	92.27 134	68.73 41	95.19 170	75.94 143	83.27 149	94.81 86

PAPR		85.15 63	84.47 68	87.18 42	96.02 25	68.29 74	91.85 162	93.00 99	76.59 113	79.03 116	95.00 63	61.59 118	97.61 58	78.16 131	89.00 100	95.63 48

MP-MVS		85.02 64	84.97 63	85.17 109	92.60 82	64.27 180	93.24 102	92.27 122	73.13 162	79.63 108	94.43 80	61.90 113	97.17 83	85.00 77	92.56 59	94.06 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 65	84.44 69	86.71 56	88.33 183	68.73 64	90.24 225	91.82 146	81.05 44	81.18 90	92.50 126	63.69 91	96.08 132	84.45 83	86.71 125	95.32 61

CHOSEN 1792x2688		84.98 66	83.45 82	89.57 10	89.94 142	75.14 5	92.07 150	92.32 120	81.87 31	75.68 151	88.27 196	60.18 131	98.60 27	80.46 113	90.27 91	94.96 77

EIA-MVS		84.84 67	84.88 64	84.69 127	91.30 117	62.36 229	93.85 77	92.04 132	79.45 63	79.33 113	94.28 90	62.42 108	96.35 121	80.05 114	91.25 81	95.38 56

fmvsm_s_conf0.1_n_a		84.76 68	84.84 66	84.53 134	80.23 311	63.50 204	92.79 117	88.73 271	80.46 49	89.84 26	96.65 22	60.96 124	97.57 61	93.80 13	80.14 175	92.53 164

HFP-MVS		84.73 69	84.40 70	85.72 90	93.75 51	65.01 159	93.50 96	93.19 90	72.19 187	79.22 114	94.93 66	59.04 146	97.67 51	81.55 102	92.21 62	94.49 103

MVS		84.66 70	82.86 97	90.06 2	90.93 123	74.56 6	87.91 271	95.54 12	68.55 259	72.35 193	94.71 73	59.78 137	98.90 19	81.29 108	94.69 31	96.74 13

GST-MVS		84.63 71	84.29 71	85.66 92	92.82 74	65.27 151	93.04 109	93.13 93	73.20 160	78.89 117	94.18 93	59.41 142	97.85 45	81.45 104	92.48 61	93.86 127

EC-MVSNet		84.53 72	85.04 62	83.01 175	89.34 154	61.37 250	94.42 51	91.09 177	77.91 91	83.24 74	94.20 92	58.37 150	95.40 162	85.35 73	91.41 77	92.27 174

ACMMPR		84.37 73	84.06 72	85.28 104	93.56 54	64.37 175	93.50 96	93.15 92	72.19 187	78.85 122	94.86 69	56.69 172	97.45 65	81.55 102	92.20 63	94.02 120

region2R		84.36 74	84.03 73	85.36 101	93.54 55	64.31 178	93.43 99	92.95 100	72.16 190	78.86 121	94.84 70	56.97 167	97.53 63	81.38 106	92.11 65	94.24 107

LFMVS		84.34 75	82.73 99	89.18 12	94.76 33	73.25 9	94.99 42	91.89 140	71.90 195	82.16 83	93.49 108	47.98 259	97.05 89	82.55 96	84.82 137	97.25 7

test_yl		84.28 76	83.16 90	87.64 30	94.52 37	69.24 52	95.78 18	95.09 21	69.19 251	81.09 91	92.88 120	57.00 165	97.44 66	81.11 109	81.76 162	96.23 33

DCV-MVSNet		84.28 76	83.16 90	87.64 30	94.52 37	69.24 52	95.78 18	95.09 21	69.19 251	81.09 91	92.88 120	57.00 165	97.44 66	81.11 109	81.76 162	96.23 33

diffmvs		84.28 76	83.83 74	85.61 93	87.40 208	68.02 84	90.88 204	89.24 245	80.54 47	81.64 86	92.52 125	59.83 136	94.52 198	87.32 58	85.11 135	94.29 105
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 76	83.36 88	87.02 48	92.22 89	67.74 90	84.65 297	94.50 39	79.15 71	82.23 82	87.93 205	66.88 55	96.94 103	80.53 112	82.20 158	96.39 28

ETVMVS		84.22 80	83.71 75	85.76 88	92.58 83	68.25 78	92.45 136	95.53 13	79.54 62	79.46 110	91.64 146	70.29 36	94.18 211	69.16 203	82.76 154	94.84 83

MAR-MVS		84.18 81	83.43 83	86.44 67	96.25 21	65.93 137	94.28 55	94.27 52	74.41 135	79.16 115	95.61 45	53.99 203	98.88 21	69.62 197	93.26 52	94.50 102
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 82	83.20 89	87.05 47	91.56 110	69.82 40	89.99 234	92.05 131	77.77 93	82.84 77	86.57 224	63.93 87	96.09 129	74.91 153	89.18 99	95.25 69

CANet_DTU		84.09 83	83.52 77	85.81 85	90.30 135	66.82 114	91.87 160	89.01 259	85.27 9	86.09 48	93.74 101	47.71 263	96.98 98	77.90 133	89.78 95	93.65 132

ET-MVSNet_ETH3D		84.01 84	83.15 92	86.58 62	90.78 128	70.89 24	94.74 47	94.62 36	81.44 38	58.19 323	93.64 104	73.64 23	92.35 276	82.66 94	78.66 190	96.50 24

PVSNet_Blended_VisFu		83.97 85	83.50 79	85.39 99	90.02 140	66.59 122	93.77 84	91.73 148	77.43 102	77.08 141	89.81 178	63.77 90	96.97 100	79.67 116	88.21 106	92.60 161

MTAPA		83.91 86	83.38 87	85.50 95	91.89 102	65.16 155	81.75 320	92.23 123	75.32 126	80.53 98	95.21 60	56.06 180	97.16 85	84.86 80	92.55 60	94.18 109

XVS		83.87 87	83.47 81	85.05 110	93.22 61	63.78 189	92.92 114	92.66 110	73.99 143	78.18 126	94.31 89	55.25 186	97.41 68	79.16 121	91.58 74	93.95 122

Effi-MVS+		83.82 88	82.76 98	86.99 49	89.56 150	69.40 47	91.35 186	86.12 313	72.59 174	83.22 75	92.81 123	59.60 139	96.01 137	81.76 101	87.80 110	95.56 51

test_fmvsmvis_n_1920		83.80 89	83.48 80	84.77 122	82.51 287	63.72 193	91.37 184	83.99 333	81.42 39	77.68 131	95.74 42	58.37 150	97.58 59	93.38 14	86.87 119	93.00 152

EI-MVSNet-Vis-set		83.77 90	83.67 76	84.06 150	92.79 77	63.56 201	91.76 167	94.81 28	79.65 61	77.87 129	94.09 94	63.35 99	97.90 42	79.35 119	79.36 182	90.74 199

MVSFormer		83.75 91	82.88 96	86.37 70	89.24 161	71.18 19	89.07 253	90.69 188	65.80 280	87.13 39	94.34 87	64.99 72	92.67 262	72.83 164	91.80 70	95.27 66

CP-MVS		83.71 92	83.40 86	84.65 129	93.14 66	63.84 187	94.59 49	92.28 121	71.03 225	77.41 135	94.92 67	55.21 189	96.19 125	81.32 107	90.70 86	93.91 124

test_fmvsmconf0.01_n		83.70 93	83.52 77	84.25 146	75.26 353	61.72 244	92.17 143	87.24 302	82.36 26	84.91 61	95.41 48	55.60 184	96.83 109	92.85 17	85.87 131	94.21 108

baseline2		83.68 94	83.42 85	84.48 137	87.37 209	66.00 134	90.06 229	95.93 8	79.71 60	69.08 229	90.39 166	77.92 6	96.28 123	78.91 125	81.38 166	91.16 195

thisisatest0515		83.41 95	82.49 104	86.16 75	89.46 153	68.26 76	93.54 94	94.70 32	74.31 138	75.75 149	90.92 156	72.62 28	96.52 119	69.64 195	81.50 165	93.71 130

PVSNet_BlendedMVS		83.38 96	83.43 83	83.22 172	93.76 49	67.53 97	94.06 63	93.61 72	79.13 72	81.00 94	85.14 239	63.19 101	97.29 76	87.08 61	73.91 228	84.83 298

test2506		83.29 97	82.92 95	84.37 141	88.39 181	63.18 212	92.01 153	91.35 165	77.66 96	78.49 125	91.42 149	64.58 80	95.09 172	73.19 160	89.23 97	94.85 80

iter_conf05		83.27 98	82.70 100	84.98 113	93.32 59	71.84 15	94.16 58	81.76 344	82.74 21	73.83 173	88.40 192	72.77 27	94.61 189	82.10 98	75.21 217	88.48 231

PGM-MVS		83.25 99	82.70 100	84.92 114	92.81 76	64.07 184	90.44 216	92.20 127	71.28 219	77.23 138	94.43 80	55.17 190	97.31 75	79.33 120	91.38 78	93.37 138

HPM-MVS		83.25 99	82.95 94	84.17 148	92.25 88	62.88 221	90.91 201	91.86 142	70.30 237	77.12 139	93.96 98	56.75 170	96.28 123	82.04 99	91.34 80	93.34 139
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
EI-MVSNet-UG-set		83.14 101	82.96 93	83.67 161	92.28 87	63.19 211	91.38 183	94.68 33	79.22 69	76.60 144	93.75 100	62.64 106	97.76 48	78.07 132	78.01 193	90.05 208

VDD-MVS		83.06 102	81.81 113	86.81 53	90.86 126	67.70 91	95.40 29	91.50 160	75.46 123	81.78 85	92.34 133	40.09 301	97.13 87	86.85 64	82.04 159	95.60 49

h-mvs33		83.01 103	82.56 103	84.35 142	89.34 154	62.02 236	92.72 120	93.76 65	81.45 36	82.73 79	92.25 136	60.11 132	97.13 87	87.69 53	62.96 307	93.91 124

PAPM_NR		82.97 104	81.84 112	86.37 70	94.10 44	66.76 117	87.66 276	92.84 103	69.96 241	74.07 170	93.57 106	63.10 103	97.50 64	70.66 188	90.58 88	94.85 80

mPP-MVS		82.96 105	82.44 105	84.52 135	92.83 72	62.92 219	92.76 118	91.85 144	71.52 215	75.61 154	94.24 91	53.48 211	96.99 97	78.97 124	90.73 85	93.64 133

SR-MVS		82.81 106	82.58 102	83.50 166	93.35 58	61.16 253	92.23 142	91.28 169	64.48 289	81.27 88	95.28 54	53.71 207	95.86 139	82.87 93	88.77 102	93.49 136

DP-MVS Recon		82.73 107	81.65 114	85.98 78	97.31 4	67.06 108	95.15 36	91.99 134	69.08 254	76.50 146	93.89 99	54.48 198	98.20 35	70.76 186	85.66 133	92.69 158

CLD-MVS		82.73 107	82.35 107	83.86 154	87.90 196	67.65 93	95.45 28	92.18 129	85.06 10	72.58 186	92.27 134	52.46 219	95.78 141	84.18 84	79.06 185	88.16 237
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 109	82.38 106	83.73 158	89.25 158	59.58 281	92.24 141	94.89 25	77.96 89	79.86 105	92.38 131	56.70 171	97.05 89	77.26 136	80.86 170	94.55 96

3Dnovator	73.91 6	82.69 110	80.82 125	88.31 23	89.57 149	71.26 18	92.60 129	94.39 47	78.84 79	67.89 250	92.48 129	48.42 254	98.52 28	68.80 208	94.40 34	95.15 71

MVSTER		82.47 111	82.05 108	83.74 156	92.68 79	69.01 58	91.90 159	93.21 87	79.83 56	72.14 194	85.71 236	74.72 16	94.72 184	75.72 144	72.49 239	87.50 242

TESTMET0.1,1		82.41 112	81.98 111	83.72 159	88.08 190	63.74 191	92.70 122	93.77 64	79.30 67	77.61 133	87.57 211	58.19 153	94.08 215	73.91 159	86.68 126	93.33 141

CostFormer		82.33 113	81.15 118	85.86 83	89.01 166	68.46 70	82.39 317	93.01 97	75.59 121	80.25 101	81.57 282	72.03 32	94.96 176	79.06 123	77.48 201	94.16 111

API-MVS		82.28 114	80.53 132	87.54 35	96.13 22	70.59 27	93.63 90	91.04 183	65.72 282	75.45 156	92.83 122	56.11 179	98.89 20	64.10 251	89.75 96	93.15 145

IB-MVS	77.80 4	82.18 115	80.46 134	87.35 39	89.14 163	70.28 31	95.59 26	95.17 19	78.85 78	70.19 217	85.82 234	70.66 35	97.67 51	72.19 175	66.52 280	94.09 115
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		82.16 116	81.12 119	85.26 106	86.42 226	68.72 65	92.59 131	90.44 198	73.12 163	84.20 67	94.36 82	38.04 315	95.73 145	84.12 85	86.81 120	91.33 188

xiu_mvs_v1_base		82.16 116	81.12 119	85.26 106	86.42 226	68.72 65	92.59 131	90.44 198	73.12 163	84.20 67	94.36 82	38.04 315	95.73 145	84.12 85	86.81 120	91.33 188

xiu_mvs_v1_base_debi		82.16 116	81.12 119	85.26 106	86.42 226	68.72 65	92.59 131	90.44 198	73.12 163	84.20 67	94.36 82	38.04 315	95.73 145	84.12 85	86.81 120	91.33 188

3Dnovator+	73.60 7	82.10 119	80.60 131	86.60 60	90.89 125	66.80 116	95.20 34	93.44 81	74.05 142	67.42 256	92.49 128	49.46 244	97.65 55	70.80 185	91.68 72	95.33 59

MVS_111021_LR		82.02 120	81.52 115	83.51 165	88.42 179	62.88 221	89.77 238	88.93 263	76.78 109	75.55 155	93.10 111	50.31 236	95.38 164	83.82 89	87.02 118	92.26 175

PMMVS		81.98 121	82.04 109	81.78 208	89.76 146	56.17 318	91.13 197	90.69 188	77.96 89	80.09 103	93.57 106	46.33 273	94.99 175	81.41 105	87.46 114	94.17 110

baseline1		81.84 122	81.03 123	84.28 145	91.60 108	66.62 120	91.08 198	91.66 154	81.87 31	74.86 160	91.67 145	69.98 38	94.92 179	71.76 178	64.75 295	91.29 193

EPP-MVSNet		81.79 123	81.52 115	82.61 184	88.77 172	60.21 273	93.02 111	93.66 71	68.52 260	72.90 181	90.39 166	72.19 31	94.96 176	74.93 152	79.29 184	92.67 159

iter_conf_final		81.74 124	80.93 124	84.18 147	92.66 80	69.10 55	92.94 113	82.80 342	79.01 77	74.85 161	88.40 192	61.83 116	94.61 189	79.36 118	76.52 210	88.83 222

test_vis1_n_1920		81.66 125	82.01 110	80.64 235	82.24 290	55.09 326	94.76 46	86.87 304	81.67 34	84.40 66	94.63 75	38.17 312	94.67 188	91.98 26	83.34 148	92.16 178

APD-MVS_3200maxsize		81.64 126	81.32 117	82.59 185	92.36 85	58.74 293	91.39 181	91.01 184	63.35 298	79.72 107	94.62 76	51.82 222	96.14 127	79.71 115	87.93 109	92.89 156

ACMMP		81.49 127	80.67 128	83.93 153	91.71 106	62.90 220	92.13 145	92.22 126	71.79 202	71.68 201	93.49 108	50.32 235	96.96 101	78.47 129	84.22 146	91.93 180
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		81.43 128	80.74 126	83.52 163	86.26 230	64.45 169	92.09 148	90.65 192	75.83 120	73.95 172	89.81 178	63.97 86	92.91 252	71.27 181	82.82 152	93.20 144
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 129	79.99 139	85.46 96	90.39 134	68.40 71	86.88 287	90.61 193	74.41 135	70.31 216	84.67 245	63.79 89	92.32 277	73.13 161	85.70 132	95.67 46

ECVR-MVS		81.29 130	80.38 135	84.01 152	88.39 181	61.96 238	92.56 134	86.79 306	77.66 96	76.63 143	91.42 149	46.34 272	95.24 169	74.36 157	89.23 97	94.85 80

thisisatest0530		81.15 131	80.07 136	84.39 140	88.26 185	65.63 143	91.40 179	94.62 36	71.27 220	70.93 207	89.18 183	72.47 29	96.04 134	65.62 240	76.89 207	91.49 184

Fast-Effi-MVS+		81.14 132	80.01 138	84.51 136	90.24 136	65.86 138	94.12 62	89.15 251	73.81 150	75.37 157	88.26 197	57.26 160	94.53 197	66.97 225	84.92 136	93.15 145

HQP-MVS		81.14 132	80.64 129	82.64 183	87.54 204	63.66 198	94.06 63	91.70 152	79.80 57	74.18 166	90.30 168	51.63 226	95.61 153	77.63 134	78.90 186	88.63 227

hse-mvs2		81.12 134	81.11 122	81.16 222	86.52 225	57.48 308	89.40 246	91.16 172	81.45 36	82.73 79	90.49 164	60.11 132	94.58 191	87.69 53	60.41 334	91.41 187

SR-MVS-dyc-post		81.06 135	80.70 127	82.15 199	92.02 94	58.56 295	90.90 202	90.45 195	62.76 305	78.89 117	94.46 78	51.26 230	95.61 153	78.77 127	86.77 123	92.28 171

HyFIR lowres test		81.03 136	79.56 146	85.43 97	87.81 200	68.11 82	90.18 226	90.01 219	70.65 233	72.95 180	86.06 232	63.61 94	94.50 199	75.01 151	79.75 179	93.67 131

nrg030		80.93 137	79.86 141	84.13 149	83.69 274	68.83 62	93.23 103	91.20 170	75.55 122	75.06 159	88.22 200	63.04 104	94.74 183	81.88 100	66.88 277	88.82 225

Vis-MVSNet		80.92 138	79.98 140	83.74 156	88.48 176	61.80 240	93.44 98	88.26 288	73.96 146	77.73 130	91.76 142	49.94 240	94.76 181	65.84 237	90.37 90	94.65 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 139	80.02 137	83.33 169	87.87 197	60.76 261	92.62 127	86.86 305	77.86 92	75.73 150	91.39 151	46.35 271	94.70 187	72.79 166	88.68 103	94.52 100

1314		80.70 140	78.95 157	85.94 80	87.77 202	67.56 95	87.91 271	92.55 116	72.17 189	67.44 255	93.09 112	50.27 237	97.04 92	71.68 180	87.64 112	93.23 143

tpmrst		80.57 141	79.14 156	84.84 117	90.10 139	68.28 75	81.70 321	89.72 231	77.63 98	75.96 148	79.54 314	64.94 74	92.71 259	75.43 146	77.28 204	93.55 134

1112_ss		80.56 142	79.83 142	82.77 179	88.65 173	60.78 259	92.29 139	88.36 282	72.58 175	72.46 190	94.95 64	65.09 71	93.42 239	66.38 231	77.71 195	94.10 114

VDDNet		80.50 143	78.26 165	87.21 41	86.19 231	69.79 41	94.48 50	91.31 166	60.42 323	79.34 112	90.91 157	38.48 310	96.56 117	82.16 97	81.05 168	95.27 66

BH-w/o		80.49 144	79.30 153	84.05 151	90.83 127	64.36 177	93.60 91	89.42 239	74.35 137	69.09 228	90.15 173	55.23 188	95.61 153	64.61 248	86.43 129	92.17 177

test_cas_vis1_n_1920		80.45 145	80.61 130	79.97 253	78.25 337	57.01 314	94.04 67	88.33 283	79.06 75	82.81 78	93.70 102	38.65 307	91.63 291	90.82 35	79.81 177	91.27 194

TAMVS		80.37 146	79.45 149	83.13 174	85.14 251	63.37 206	91.23 192	90.76 187	74.81 133	72.65 184	88.49 189	60.63 127	92.95 247	69.41 199	81.95 161	93.08 148

HQP_MVS		80.34 147	79.75 143	82.12 201	86.94 219	62.42 227	93.13 105	91.31 166	78.81 80	72.53 187	89.14 185	50.66 233	95.55 158	76.74 137	78.53 191	88.39 234

SDMVSNet		80.26 148	78.88 158	84.40 139	89.25 158	67.63 94	85.35 293	93.02 96	76.77 110	70.84 208	87.12 218	47.95 260	96.09 129	85.04 76	74.55 219	89.48 218

HPM-MVS_fast		80.25 149	79.55 148	82.33 191	91.55 111	59.95 276	91.32 188	89.16 250	65.23 286	74.71 163	93.07 114	47.81 262	95.74 144	74.87 155	88.23 105	91.31 192

ab-mvs		80.18 150	78.31 164	85.80 86	88.44 178	65.49 149	83.00 314	92.67 109	71.82 201	77.36 136	85.01 240	54.50 195	96.59 114	76.35 141	75.63 215	95.32 61

IS-MVSNet		80.14 151	79.41 150	82.33 191	87.91 195	60.08 275	91.97 157	88.27 286	72.90 170	71.44 204	91.73 144	61.44 119	93.66 234	62.47 265	86.53 127	93.24 142

test-LLR		80.10 152	79.56 146	81.72 210	86.93 221	61.17 251	92.70 122	91.54 157	71.51 216	75.62 152	86.94 220	53.83 204	92.38 273	72.21 173	84.76 139	91.60 182

PVSNet	73.49 8	80.05 153	78.63 160	84.31 143	90.92 124	64.97 160	92.47 135	91.05 182	79.18 70	72.43 191	90.51 163	37.05 327	94.06 217	68.06 212	86.00 130	93.90 126

UA-Net		80.02 154	79.65 144	81.11 224	89.33 156	57.72 303	86.33 290	89.00 262	77.44 101	81.01 93	89.15 184	59.33 143	95.90 138	61.01 272	84.28 144	89.73 214

test-mter		79.96 155	79.38 152	81.72 210	86.93 221	61.17 251	92.70 122	91.54 157	73.85 148	75.62 152	86.94 220	49.84 242	92.38 273	72.21 173	84.76 139	91.60 182

QAPM		79.95 156	77.39 182	87.64 30	89.63 148	71.41 17	93.30 101	93.70 69	65.34 285	67.39 258	91.75 143	47.83 261	98.96 16	57.71 288	89.81 93	92.54 163

UGNet		79.87 157	78.68 159	83.45 168	89.96 141	61.51 247	92.13 145	90.79 186	76.83 108	78.85 122	86.33 228	38.16 313	96.17 126	67.93 215	87.17 117	92.67 159
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		79.80 158	77.95 171	85.34 102	88.28 184	68.26 76	81.56 323	91.42 163	70.11 239	77.59 134	80.50 300	67.40 52	94.26 208	67.34 220	77.35 202	93.51 135

thres200		79.66 159	78.33 163	83.66 162	92.54 84	65.82 140	93.06 107	96.31 3	74.90 132	73.30 177	88.66 187	59.67 138	95.61 153	47.84 326	78.67 189	89.56 217

CPTT-MVS		79.59 160	79.16 155	80.89 233	91.54 112	59.80 278	92.10 147	88.54 279	60.42 323	72.96 179	93.28 110	48.27 255	92.80 256	78.89 126	86.50 128	90.06 207

Test_1112_low_res		79.56 161	78.60 161	82.43 187	88.24 187	60.39 270	92.09 148	87.99 293	72.10 191	71.84 197	87.42 213	64.62 79	93.04 243	65.80 238	77.30 203	93.85 128

tttt0517		79.50 162	78.53 162	82.41 190	87.22 212	61.43 249	89.75 239	94.76 29	69.29 249	67.91 248	88.06 204	72.92 25	95.63 151	62.91 261	73.90 229	90.16 206

FIs		79.47 163	79.41 150	79.67 260	85.95 236	59.40 283	91.68 171	93.94 59	78.06 88	68.96 233	88.28 195	66.61 58	91.77 288	66.20 234	74.99 218	87.82 239

BH-RMVSNet		79.46 164	77.65 174	84.89 115	91.68 107	65.66 141	93.55 93	88.09 291	72.93 167	73.37 176	91.12 155	46.20 275	96.12 128	56.28 293	85.61 134	92.91 154

PCF-MVS	73.15 9	79.29 165	77.63 175	84.29 144	86.06 234	65.96 136	87.03 283	91.10 176	69.86 243	69.79 224	90.64 159	57.54 159	96.59 114	64.37 250	82.29 155	90.32 204
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 166	79.57 145	78.24 281	88.46 177	52.29 337	90.41 218	89.12 253	74.24 139	69.13 227	91.91 140	65.77 65	90.09 313	59.00 284	88.09 107	92.33 168

114514_t		79.17 167	77.67 173	83.68 160	95.32 29	65.53 147	92.85 116	91.60 156	63.49 296	67.92 247	90.63 161	46.65 268	95.72 149	67.01 224	83.54 147	89.79 212

FA-MVS(test-final)		79.12 168	77.23 184	84.81 121	90.54 130	63.98 186	81.35 326	91.71 150	71.09 224	74.85 161	82.94 263	52.85 215	97.05 89	67.97 213	81.73 164	93.41 137

VPA-MVSNet		79.03 169	78.00 169	82.11 204	85.95 236	64.48 168	93.22 104	94.66 34	75.05 130	74.04 171	84.95 241	52.17 221	93.52 236	74.90 154	67.04 276	88.32 236

OPM-MVS		79.00 170	78.09 167	81.73 209	83.52 277	63.83 188	91.64 173	90.30 205	76.36 116	71.97 196	89.93 177	46.30 274	95.17 171	75.10 149	77.70 196	86.19 270
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 171	78.22 166	81.25 219	85.33 246	62.73 224	89.53 243	93.21 87	72.39 182	72.14 194	90.13 174	60.99 122	94.72 184	67.73 217	72.49 239	86.29 266

AdaColmap		78.94 172	77.00 188	84.76 123	96.34 17	65.86 138	92.66 126	87.97 295	62.18 310	70.56 210	92.37 132	43.53 289	97.35 72	64.50 249	82.86 151	91.05 197

GeoE		78.90 173	77.43 178	83.29 170	88.95 167	62.02 236	92.31 138	86.23 311	70.24 238	71.34 205	89.27 182	54.43 199	94.04 220	63.31 257	80.81 172	93.81 129

miper_enhance_ethall		78.86 174	77.97 170	81.54 214	88.00 194	65.17 154	91.41 177	89.15 251	75.19 128	68.79 236	83.98 254	67.17 53	92.82 254	72.73 167	65.30 286	86.62 263

VPNet		78.82 175	77.53 177	82.70 181	84.52 261	66.44 124	93.93 72	92.23 123	80.46 49	72.60 185	88.38 194	49.18 248	93.13 242	72.47 171	63.97 304	88.55 230

EPNet_dtu		78.80 176	79.26 154	77.43 289	88.06 191	49.71 350	91.96 158	91.95 136	77.67 95	76.56 145	91.28 153	58.51 149	90.20 311	56.37 292	80.95 169	92.39 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 177	77.43 178	82.88 177	92.21 90	64.49 166	92.05 151	96.28 4	73.48 157	71.75 199	88.26 197	60.07 134	95.32 165	45.16 337	77.58 198	88.83 222

TR-MVS		78.77 178	77.37 183	82.95 176	90.49 131	60.88 257	93.67 88	90.07 214	70.08 240	74.51 164	91.37 152	45.69 278	95.70 150	60.12 278	80.32 174	92.29 170

thres400		78.68 179	77.43 178	82.43 187	92.21 90	64.49 166	92.05 151	96.28 4	73.48 157	71.75 199	88.26 197	60.07 134	95.32 165	45.16 337	77.58 198	87.48 243

BH-untuned		78.68 179	77.08 185	83.48 167	89.84 143	63.74 191	92.70 122	88.59 277	71.57 213	66.83 265	88.65 188	51.75 224	95.39 163	59.03 283	84.77 138	91.32 191

OMC-MVS		78.67 181	77.91 172	80.95 231	85.76 241	57.40 310	88.49 262	88.67 274	73.85 148	72.43 191	92.10 137	49.29 247	94.55 196	72.73 167	77.89 194	90.91 198

tpm		78.58 182	77.03 186	83.22 172	85.94 238	64.56 164	83.21 311	91.14 175	78.31 85	73.67 174	79.68 312	64.01 85	92.09 282	66.07 235	71.26 249	93.03 150

OpenMVS	70.45 11	78.54 183	75.92 202	86.41 69	85.93 239	71.68 16	92.74 119	92.51 117	66.49 276	64.56 280	91.96 139	43.88 288	98.10 37	54.61 298	90.65 87	89.44 220

EPMVS		78.49 184	75.98 201	86.02 77	91.21 119	69.68 45	80.23 335	91.20 170	75.25 127	72.48 189	78.11 322	54.65 194	93.69 233	57.66 289	83.04 150	94.69 88

AUN-MVS		78.37 185	77.43 178	81.17 221	86.60 224	57.45 309	89.46 245	91.16 172	74.11 141	74.40 165	90.49 164	55.52 185	94.57 193	74.73 156	60.43 333	91.48 185

thres100view900		78.37 185	77.01 187	82.46 186	91.89 102	63.21 210	91.19 196	96.33 1	72.28 185	70.45 213	87.89 206	60.31 129	95.32 165	45.16 337	77.58 198	88.83 222

GA-MVS		78.33 187	76.23 197	84.65 129	83.65 275	66.30 128	91.44 175	90.14 212	76.01 118	70.32 215	84.02 253	42.50 293	94.72 184	70.98 183	77.00 206	92.94 153

cascas		78.18 188	75.77 204	85.41 98	87.14 214	69.11 54	92.96 112	91.15 174	66.71 274	70.47 211	86.07 231	37.49 321	96.48 120	70.15 191	79.80 178	90.65 200

UniMVSNet_NR-MVSNet		78.15 189	77.55 176	79.98 251	84.46 263	60.26 271	92.25 140	93.20 89	77.50 100	68.88 234	86.61 223	66.10 61	92.13 280	66.38 231	62.55 311	87.54 241

thres600view7		78.00 190	76.66 192	82.03 206	91.93 99	63.69 196	91.30 189	96.33 1	72.43 180	70.46 212	87.89 206	60.31 129	94.92 179	42.64 349	76.64 208	87.48 243

FC-MVSNet-test		77.99 191	78.08 168	77.70 284	84.89 256	55.51 323	90.27 223	93.75 68	76.87 105	66.80 266	87.59 210	65.71 66	90.23 310	62.89 262	73.94 227	87.37 246

Anonymous202405211		77.96 192	75.33 211	85.87 82	93.73 52	64.52 165	94.85 44	85.36 319	62.52 308	76.11 147	90.18 171	29.43 356	97.29 76	68.51 210	77.24 205	95.81 45

cl22		77.94 193	76.78 190	81.42 216	87.57 203	64.93 162	90.67 211	88.86 266	72.45 179	67.63 254	82.68 267	64.07 84	92.91 252	71.79 176	65.30 286	86.44 264

XXY-MVS		77.94 193	76.44 194	82.43 187	82.60 286	64.44 170	92.01 153	91.83 145	73.59 156	70.00 220	85.82 234	54.43 199	94.76 181	69.63 196	68.02 270	88.10 238

MS-PatchMatch		77.90 195	76.50 193	82.12 201	85.99 235	69.95 36	91.75 169	92.70 107	73.97 145	62.58 301	84.44 249	41.11 298	95.78 141	63.76 254	92.17 64	80.62 345

FMVSNet3		77.73 196	76.04 200	82.80 178	91.20 120	68.99 59	91.87 160	91.99 134	73.35 159	67.04 261	83.19 262	56.62 173	92.14 279	59.80 280	69.34 257	87.28 250

miper_ehance_all_eth		77.60 197	76.44 194	81.09 228	85.70 243	64.41 173	90.65 212	88.64 276	72.31 183	67.37 259	82.52 268	64.77 78	92.64 266	70.67 187	65.30 286	86.24 268

UniMVSNet (Re)		77.58 198	76.78 190	79.98 251	84.11 269	60.80 258	91.76 167	93.17 91	76.56 114	69.93 223	84.78 244	63.32 100	92.36 275	64.89 247	62.51 313	86.78 258

PatchmatchNet		77.46 199	74.63 217	85.96 79	89.55 151	70.35 30	79.97 340	89.55 234	72.23 186	70.94 206	76.91 333	57.03 163	92.79 257	54.27 300	81.17 167	94.74 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 200	75.65 207	82.73 180	80.38 307	67.13 107	91.85 162	90.23 209	75.09 129	69.37 225	83.39 260	53.79 206	94.44 200	71.77 177	65.00 292	86.63 262

CHOSEN 280x420		77.35 201	76.95 189	78.55 276	87.07 216	62.68 225	69.71 368	82.95 340	68.80 256	71.48 203	87.27 217	66.03 62	84.00 355	76.47 140	82.81 153	88.95 221

PS-MVSNAJss		77.26 202	76.31 196	80.13 246	80.64 305	59.16 288	90.63 215	91.06 181	72.80 171	68.58 240	84.57 247	53.55 208	93.96 225	72.97 162	71.96 243	87.27 251

gg-mvs-nofinetune		77.18 203	74.31 224	85.80 86	91.42 114	68.36 72	71.78 362	94.72 31	49.61 363	77.12 139	45.92 386	77.41 8	93.98 224	67.62 218	93.16 53	95.05 74

WB-MVSnew		77.14 204	76.18 199	80.01 250	86.18 232	63.24 209	91.26 190	94.11 56	71.72 205	73.52 175	87.29 216	45.14 283	93.00 245	56.98 290	79.42 180	83.80 306

MVP-Stereo		77.12 205	76.23 197	79.79 258	81.72 295	66.34 127	89.29 247	90.88 185	70.56 235	62.01 304	82.88 264	49.34 245	94.13 212	65.55 242	93.80 41	78.88 359
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 206	75.37 209	82.20 197	89.25 158	62.11 235	82.06 318	89.09 255	76.77 110	70.84 208	87.12 218	41.43 297	95.01 174	67.23 222	74.55 219	89.48 218

dmvs_re		76.93 207	75.36 210	81.61 212	87.78 201	60.71 264	80.00 339	87.99 293	79.42 64	69.02 231	89.47 181	46.77 266	94.32 202	63.38 256	74.45 222	89.81 211

X-MVStestdata		76.86 208	74.13 228	85.05 110	93.22 61	63.78 189	92.92 114	92.66 110	73.99 143	78.18 126	10.19 401	55.25 186	97.41 68	79.16 121	91.58 74	93.95 122

DU-MVS		76.86 208	75.84 203	79.91 254	82.96 283	60.26 271	91.26 190	91.54 157	76.46 115	68.88 234	86.35 226	56.16 177	92.13 280	66.38 231	62.55 311	87.35 248

mvsmamba		76.85 210	75.71 206	80.25 243	83.07 282	59.16 288	91.44 175	80.64 349	76.84 107	67.95 246	86.33 228	46.17 276	94.24 209	76.06 142	72.92 235	87.36 247

Anonymous20240529		76.84 211	74.15 227	84.88 116	91.02 121	64.95 161	93.84 80	91.09 177	53.57 352	73.00 178	87.42 213	35.91 331	97.32 74	69.14 204	72.41 241	92.36 167

c3_l		76.83 212	75.47 208	80.93 232	85.02 254	64.18 183	90.39 219	88.11 290	71.66 206	66.65 267	81.64 280	63.58 96	92.56 267	69.31 201	62.86 308	86.04 275

WR-MVS		76.76 213	75.74 205	79.82 257	84.60 259	62.27 233	92.60 129	92.51 117	76.06 117	67.87 251	85.34 237	56.76 169	90.24 309	62.20 266	63.69 306	86.94 256

v1144		76.73 214	74.88 214	82.27 193	80.23 311	66.60 121	91.68 171	90.21 211	73.69 153	69.06 230	81.89 275	52.73 217	94.40 201	69.21 202	65.23 289	85.80 281

IterMVS-LS		76.49 215	75.18 213	80.43 238	84.49 262	62.74 223	90.64 213	88.80 268	72.40 181	65.16 274	81.72 278	60.98 123	92.27 278	67.74 216	64.65 297	86.29 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 216	74.55 220	82.19 198	79.14 325	67.82 88	90.26 224	89.42 239	73.75 151	68.63 239	81.89 275	51.31 229	94.09 214	71.69 179	64.84 293	84.66 299

v148		76.19 217	74.47 222	81.36 217	80.05 313	64.44 170	91.75 169	90.23 209	73.68 154	67.13 260	80.84 295	55.92 182	93.86 231	68.95 206	61.73 322	85.76 284

Effi-MVS+-dtu		76.14 218	75.28 212	78.72 275	83.22 279	55.17 325	89.87 235	87.78 296	75.42 124	67.98 245	81.43 284	45.08 284	92.52 269	75.08 150	71.63 244	88.48 231

cl____		76.07 219	74.67 215	80.28 241	85.15 250	61.76 242	90.12 227	88.73 271	71.16 221	65.43 271	81.57 282	61.15 120	92.95 247	66.54 228	62.17 315	86.13 273

DIV-MVS_self_test		76.07 219	74.67 215	80.28 241	85.14 251	61.75 243	90.12 227	88.73 271	71.16 221	65.42 272	81.60 281	61.15 120	92.94 251	66.54 228	62.16 317	86.14 271

FMVSNet2		76.07 219	74.01 230	82.26 195	88.85 168	67.66 92	91.33 187	91.61 155	70.84 228	65.98 268	82.25 271	48.03 256	92.00 284	58.46 285	68.73 265	87.10 253

v144192		76.05 222	74.03 229	82.12 201	79.50 319	66.55 123	91.39 181	89.71 232	72.30 184	68.17 243	81.33 287	51.75 224	94.03 222	67.94 214	64.19 299	85.77 282

NR-MVSNet		76.05 222	74.59 218	80.44 237	82.96 283	62.18 234	90.83 206	91.73 148	77.12 104	60.96 308	86.35 226	59.28 144	91.80 287	60.74 273	61.34 326	87.35 248

v1192		75.98 224	73.92 231	82.15 199	79.73 315	66.24 130	91.22 193	89.75 226	72.67 173	68.49 241	81.42 285	49.86 241	94.27 206	67.08 223	65.02 291	85.95 278

FE-MVS		75.97 225	73.02 241	84.82 118	89.78 144	65.56 145	77.44 351	91.07 180	64.55 288	72.66 183	79.85 310	46.05 277	96.69 112	54.97 297	80.82 171	92.21 176

eth_miper_zixun_eth		75.96 226	74.40 223	80.66 234	84.66 258	63.02 214	89.28 248	88.27 286	71.88 197	65.73 269	81.65 279	59.45 140	92.81 255	68.13 211	60.53 331	86.14 271

TranMVSNet+NR-MVSNet		75.86 227	74.52 221	79.89 255	82.44 288	60.64 267	91.37 184	91.37 164	76.63 112	67.65 253	86.21 230	52.37 220	91.55 293	61.84 268	60.81 329	87.48 243

SCA		75.82 228	72.76 245	85.01 112	86.63 223	70.08 32	81.06 328	89.19 248	71.60 212	70.01 219	77.09 331	45.53 279	90.25 306	60.43 275	73.27 231	94.68 89

LPG-MVS_test		75.82 228	74.58 219	79.56 264	84.31 266	59.37 284	90.44 216	89.73 229	69.49 246	64.86 275	88.42 190	38.65 307	94.30 204	72.56 169	72.76 236	85.01 296

GBi-Net		75.65 230	73.83 232	81.10 225	88.85 168	65.11 156	90.01 231	90.32 201	70.84 228	67.04 261	80.25 305	48.03 256	91.54 294	59.80 280	69.34 257	86.64 259

test1		75.65 230	73.83 232	81.10 225	88.85 168	65.11 156	90.01 231	90.32 201	70.84 228	67.04 261	80.25 305	48.03 256	91.54 294	59.80 280	69.34 257	86.64 259

v1921920		75.63 232	73.49 237	82.06 205	79.38 320	66.35 126	91.07 200	89.48 235	71.98 192	67.99 244	81.22 290	49.16 250	93.90 228	66.56 227	64.56 298	85.92 280

ACMP	71.68 10	75.58 233	74.23 226	79.62 262	84.97 255	59.64 279	90.80 207	89.07 257	70.39 236	62.95 297	87.30 215	38.28 311	93.87 229	72.89 163	71.45 247	85.36 291
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 234	73.26 239	81.61 212	80.67 304	66.82 114	89.54 242	89.27 244	71.65 207	63.30 293	80.30 304	54.99 192	94.06 217	67.33 221	62.33 314	83.94 304

tpm cat1		75.30 235	72.21 254	84.58 133	88.52 174	67.77 89	78.16 349	88.02 292	61.88 315	68.45 242	76.37 337	60.65 126	94.03 222	53.77 303	74.11 225	91.93 180

PLC	68.80 14	75.23 236	73.68 235	79.86 256	92.93 70	58.68 294	90.64 213	88.30 284	60.90 320	64.43 284	90.53 162	42.38 294	94.57 193	56.52 291	76.54 209	86.33 265
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 237	72.98 242	81.88 207	79.20 322	66.00 134	90.75 209	89.11 254	71.63 211	67.41 257	81.22 290	47.36 264	93.87 229	65.46 243	64.72 296	85.77 282

Fast-Effi-MVS+-dtu		75.04 238	73.37 238	80.07 247	80.86 300	59.52 282	91.20 195	85.38 318	71.90 195	65.20 273	84.84 243	41.46 296	92.97 246	66.50 230	72.96 234	87.73 240

dp		75.01 239	72.09 255	83.76 155	89.28 157	66.22 131	79.96 341	89.75 226	71.16 221	67.80 252	77.19 330	51.81 223	92.54 268	50.39 311	71.44 248	92.51 165

TAPA-MVS	70.22 12	74.94 240	73.53 236	79.17 269	90.40 133	52.07 338	89.19 251	89.61 233	62.69 307	70.07 218	92.67 124	48.89 253	94.32 202	38.26 363	79.97 176	91.12 196
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 241	72.54 251	81.46 215	80.33 309	66.71 118	89.15 252	89.08 256	70.94 226	63.08 296	79.86 309	52.52 218	94.04 220	65.70 239	62.17 315	83.64 307

XVG-OURS-SEG-HR		74.70 242	73.08 240	79.57 263	78.25 337	57.33 311	80.49 331	87.32 299	63.22 300	68.76 237	90.12 176	44.89 285	91.59 292	70.55 189	74.09 226	89.79 212

RRT_MVS		74.44 243	72.97 243	78.84 274	82.36 289	57.66 305	89.83 237	88.79 270	70.61 234	64.58 279	84.89 242	39.24 303	92.65 265	70.11 192	66.34 281	86.21 269

ACMM	69.62 13	74.34 244	72.73 247	79.17 269	84.25 268	57.87 301	90.36 220	89.93 220	63.17 302	65.64 270	86.04 233	37.79 319	94.10 213	65.89 236	71.52 246	85.55 287
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 245	72.30 253	80.32 239	91.49 113	61.66 245	90.85 205	80.72 348	56.67 344	63.85 288	90.64 159	46.75 267	90.84 301	53.79 302	75.99 214	88.47 233

XVG-OURS		74.25 246	72.46 252	79.63 261	78.45 335	57.59 307	80.33 333	87.39 298	63.86 293	68.76 237	89.62 180	40.50 300	91.72 289	69.00 205	74.25 224	89.58 215

test_fmvs1		74.07 247	73.69 234	75.22 308	78.91 329	47.34 362	89.06 255	74.69 363	63.68 295	79.41 111	91.59 147	24.36 365	87.77 332	85.22 74	76.26 212	90.55 203

CVMVSNet		74.04 248	74.27 225	73.33 323	85.33 246	43.94 373	89.53 243	88.39 281	54.33 351	70.37 214	90.13 174	49.17 249	84.05 353	61.83 269	79.36 182	91.99 179

Baseline_NR-MVSNet		73.99 249	72.83 244	77.48 288	80.78 302	59.29 287	91.79 164	84.55 326	68.85 255	68.99 232	80.70 296	56.16 177	92.04 283	62.67 263	60.98 328	81.11 339

pmmvs4		73.92 250	71.81 259	80.25 243	79.17 323	65.24 152	87.43 279	87.26 301	67.64 268	63.46 291	83.91 255	48.96 252	91.53 297	62.94 260	65.49 285	83.96 303

D2MVS		73.80 251	72.02 256	79.15 271	79.15 324	62.97 215	88.58 261	90.07 214	72.94 166	59.22 317	78.30 319	42.31 295	92.70 261	65.59 241	72.00 242	81.79 334

CR-MVSNet		73.79 252	70.82 267	82.70 181	83.15 280	67.96 85	70.25 365	84.00 331	73.67 155	69.97 221	72.41 351	57.82 156	89.48 317	52.99 306	73.13 232	90.64 201

test_djsdf		73.76 253	72.56 250	77.39 290	77.00 347	53.93 331	89.07 253	90.69 188	65.80 280	63.92 286	82.03 274	43.14 292	92.67 262	72.83 164	68.53 266	85.57 286

pmmvs5		73.35 254	71.52 261	78.86 273	78.64 333	60.61 268	91.08 198	86.90 303	67.69 265	63.32 292	83.64 256	44.33 287	90.53 303	62.04 267	66.02 283	85.46 289

Anonymous20231211		73.08 255	70.39 271	81.13 223	90.62 129	63.33 207	91.40 179	90.06 216	51.84 357	64.46 283	80.67 298	36.49 329	94.07 216	63.83 253	64.17 300	85.98 277

tt0805		73.07 256	70.73 268	80.07 247	78.37 336	57.05 313	87.78 273	92.18 129	61.23 319	67.04 261	86.49 225	31.35 350	94.58 191	65.06 246	67.12 275	88.57 229

miper_lstm_enhance		73.05 257	71.73 260	77.03 295	83.80 272	58.32 297	81.76 319	88.88 264	69.80 244	61.01 307	78.23 321	57.19 161	87.51 336	65.34 244	59.53 336	85.27 294

jajsoiax		73.05 257	71.51 262	77.67 285	77.46 344	54.83 327	88.81 257	90.04 217	69.13 253	62.85 299	83.51 258	31.16 351	92.75 258	70.83 184	69.80 253	85.43 290

LCM-MVSNet-Re		72.93 259	71.84 258	76.18 304	88.49 175	48.02 357	80.07 338	70.17 374	73.96 146	52.25 347	80.09 308	49.98 239	88.24 326	67.35 219	84.23 145	92.28 171

pm-mvs1		72.89 260	71.09 264	78.26 280	79.10 326	57.62 306	90.80 207	89.30 243	67.66 266	62.91 298	81.78 277	49.11 251	92.95 247	60.29 277	58.89 339	84.22 302

tpmvs		72.88 261	69.76 277	82.22 196	90.98 122	67.05 109	78.22 348	88.30 284	63.10 303	64.35 285	74.98 344	55.09 191	94.27 206	43.25 343	69.57 256	85.34 292

test0.0.03 1		72.76 262	72.71 248	72.88 327	80.25 310	47.99 358	91.22 193	89.45 237	71.51 216	62.51 302	87.66 209	53.83 204	85.06 349	50.16 313	67.84 273	85.58 285

UniMVSNet_ETH3D		72.74 263	70.53 270	79.36 266	78.62 334	56.64 316	85.01 295	89.20 247	63.77 294	64.84 277	84.44 249	34.05 338	91.86 286	63.94 252	70.89 251	89.57 216

mvs_tets		72.71 264	71.11 263	77.52 286	77.41 345	54.52 329	88.45 263	89.76 225	68.76 258	62.70 300	83.26 261	29.49 355	92.71 259	70.51 190	69.62 255	85.34 292

FMVSNet1		72.71 264	69.91 275	81.10 225	83.60 276	65.11 156	90.01 231	90.32 201	63.92 292	63.56 290	80.25 305	36.35 330	91.54 294	54.46 299	66.75 278	86.64 259

test_fmvs1_n		72.69 266	71.92 257	74.99 311	71.15 366	47.08 364	87.34 281	75.67 358	63.48 297	78.08 128	91.17 154	20.16 376	87.87 329	84.65 81	75.57 216	90.01 209

IterMVS		72.65 267	70.83 265	78.09 282	82.17 291	62.96 216	87.64 277	86.28 309	71.56 214	60.44 310	78.85 317	45.42 281	86.66 340	63.30 258	61.83 319	84.65 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 268	72.74 246	72.10 335	87.87 197	49.45 352	88.07 267	89.01 259	72.91 168	63.11 294	88.10 201	63.63 92	85.54 345	32.73 377	69.23 260	81.32 337

PatchMatch-RL		72.06 269	69.98 272	78.28 279	89.51 152	55.70 322	83.49 304	83.39 338	61.24 318	63.72 289	82.76 265	34.77 335	93.03 244	53.37 305	77.59 197	86.12 274

PVSNet_0	68.08 15	71.81 270	68.32 287	82.27 193	84.68 257	62.31 232	88.68 259	90.31 204	75.84 119	57.93 328	80.65 299	37.85 318	94.19 210	69.94 193	29.05 391	90.31 205

MIMVSNet		71.64 271	68.44 285	81.23 220	81.97 294	64.44 170	73.05 361	88.80 268	69.67 245	64.59 278	74.79 345	32.79 342	87.82 330	53.99 301	76.35 211	91.42 186

test_vis1_n		71.63 272	70.73 268	74.31 318	69.63 372	47.29 363	86.91 285	72.11 369	63.21 301	75.18 158	90.17 172	20.40 374	85.76 344	84.59 82	74.42 223	89.87 210

bld_raw_dy_0_64		71.59 273	69.71 278	77.22 294	77.82 343	58.12 299	87.71 275	73.66 365	68.01 263	61.90 306	84.29 251	33.68 339	88.43 324	69.91 194	70.43 252	85.11 295

IterMVS-SCA-FT		71.55 274	69.97 273	76.32 302	81.48 296	60.67 266	87.64 277	85.99 314	66.17 278	59.50 315	78.88 316	45.53 279	83.65 357	62.58 264	61.93 318	84.63 301

v7n		71.31 275	68.65 282	79.28 267	76.40 349	60.77 260	86.71 288	89.45 237	64.17 291	58.77 322	78.24 320	44.59 286	93.54 235	57.76 287	61.75 321	83.52 310

anonymousdsp		71.14 276	69.37 280	76.45 301	72.95 361	54.71 328	84.19 299	88.88 264	61.92 314	62.15 303	79.77 311	38.14 314	91.44 299	68.90 207	67.45 274	83.21 316

F-COLMAP		70.66 277	68.44 285	77.32 291	86.37 229	55.91 320	88.00 269	86.32 308	56.94 342	57.28 331	88.07 203	33.58 340	92.49 270	51.02 309	68.37 267	83.55 308

WR-MVS_H		70.59 278	69.94 274	72.53 329	81.03 299	51.43 341	87.35 280	92.03 133	67.38 269	60.23 312	80.70 296	55.84 183	83.45 359	46.33 333	58.58 341	82.72 323

CP-MVSNet		70.50 279	69.91 275	72.26 332	80.71 303	51.00 344	87.23 282	90.30 205	67.84 264	59.64 314	82.69 266	50.23 238	82.30 367	51.28 308	59.28 337	83.46 312

RPMNet		70.42 280	65.68 299	84.63 131	83.15 280	67.96 85	70.25 365	90.45 195	46.83 371	69.97 221	65.10 370	56.48 176	95.30 168	35.79 368	73.13 232	90.64 201

testing3		70.38 281	70.83 265	69.03 346	85.82 240	43.93 374	90.72 210	90.56 194	68.06 262	60.24 311	86.82 222	64.83 76	84.12 351	26.33 384	64.10 301	79.04 358

tfpnnormal		70.10 282	67.36 290	78.32 278	83.45 278	60.97 256	88.85 256	92.77 105	64.85 287	60.83 309	78.53 318	43.52 290	93.48 237	31.73 380	61.70 323	80.52 346

TransMVSNet (Re)		70.07 283	67.66 289	77.31 292	80.62 306	59.13 290	91.78 166	84.94 323	65.97 279	60.08 313	80.44 301	50.78 232	91.87 285	48.84 319	45.46 369	80.94 341

CL-MVSNet_self_test		69.92 284	68.09 288	75.41 307	73.25 360	55.90 321	90.05 230	89.90 221	69.96 241	61.96 305	76.54 334	51.05 231	87.64 333	49.51 317	50.59 361	82.70 325

DP-MVS		69.90 285	66.48 292	80.14 245	95.36 28	62.93 217	89.56 240	76.11 356	50.27 362	57.69 329	85.23 238	39.68 302	95.73 145	33.35 373	71.05 250	81.78 335

PS-CasMVS		69.86 286	69.13 281	72.07 336	80.35 308	50.57 346	87.02 284	89.75 226	67.27 270	59.19 318	82.28 270	46.58 269	82.24 368	50.69 310	59.02 338	83.39 314

Syy-MVS		69.65 287	69.52 279	70.03 342	87.87 197	43.21 375	88.07 267	89.01 259	72.91 168	63.11 294	88.10 201	45.28 282	85.54 345	22.07 388	69.23 260	81.32 337

MSDG		69.54 288	65.73 298	80.96 230	85.11 253	63.71 194	84.19 299	83.28 339	56.95 341	54.50 338	84.03 252	31.50 348	96.03 135	42.87 347	69.13 262	83.14 318

PEN-MVS		69.46 289	68.56 283	72.17 334	79.27 321	49.71 350	86.90 286	89.24 245	67.24 273	59.08 319	82.51 269	47.23 265	83.54 358	48.42 321	57.12 342	83.25 315

LS3D		69.17 290	66.40 294	77.50 287	91.92 100	56.12 319	85.12 294	80.37 350	46.96 369	56.50 333	87.51 212	37.25 322	93.71 232	32.52 379	79.40 181	82.68 326

PatchT		69.11 291	65.37 303	80.32 239	82.07 293	63.68 197	67.96 374	87.62 297	50.86 360	69.37 225	65.18 369	57.09 162	88.53 323	41.59 352	66.60 279	88.74 226

KD-MVS_2432*1600		69.03 292	66.37 295	77.01 296	85.56 244	61.06 254	81.44 324	90.25 207	67.27 270	58.00 326	76.53 335	54.49 196	87.63 334	48.04 323	35.77 383	82.34 329

miper_refine_blended		69.03 292	66.37 295	77.01 296	85.56 244	61.06 254	81.44 324	90.25 207	67.27 270	58.00 326	76.53 335	54.49 196	87.63 334	48.04 323	35.77 383	82.34 329

mvsany_test1		68.77 294	68.56 283	69.39 344	73.57 359	45.88 369	80.93 329	60.88 387	59.65 329	71.56 202	90.26 170	43.22 291	75.05 377	74.26 158	62.70 310	87.25 252

ACMH	63.93 17	68.62 295	64.81 305	80.03 249	85.22 249	63.25 208	87.72 274	84.66 325	60.83 321	51.57 350	79.43 315	27.29 361	94.96 176	41.76 350	64.84 293	81.88 333
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 296	65.41 302	77.96 283	78.69 332	62.93 217	89.86 236	89.17 249	60.55 322	50.27 355	77.73 325	22.60 370	94.06 217	47.18 329	72.65 238	76.88 367

ADS-MVSNet		68.54 297	64.38 312	81.03 229	88.06 191	66.90 113	68.01 372	84.02 330	57.57 336	64.48 281	69.87 361	38.68 305	89.21 319	40.87 354	67.89 271	86.97 254

DTE-MVSNet		68.46 298	67.33 291	71.87 338	77.94 341	49.00 355	86.16 291	88.58 278	66.36 277	58.19 323	82.21 272	46.36 270	83.87 356	44.97 340	55.17 349	82.73 322

our_test_3		68.29 299	64.69 307	79.11 272	78.92 327	64.85 163	88.40 264	85.06 321	60.32 325	52.68 345	76.12 339	40.81 299	89.80 316	44.25 342	55.65 347	82.67 327

Patchmatch-RL test		68.17 300	64.49 310	79.19 268	71.22 365	53.93 331	70.07 367	71.54 373	69.22 250	56.79 332	62.89 373	56.58 174	88.61 320	69.53 198	52.61 356	95.03 76

XVG-ACMP-BASELINE		68.04 301	65.53 301	75.56 306	74.06 358	52.37 336	78.43 345	85.88 315	62.03 312	58.91 321	81.21 292	20.38 375	91.15 300	60.69 274	68.18 268	83.16 317

FMVSNet5		68.04 301	65.66 300	75.18 310	84.43 264	57.89 300	83.54 303	86.26 310	61.83 316	53.64 343	73.30 348	37.15 325	85.08 348	48.99 318	61.77 320	82.56 328

ppachtmachnet_test		67.72 303	63.70 314	79.77 259	78.92 327	66.04 133	88.68 259	82.90 341	60.11 327	55.45 335	75.96 340	39.19 304	90.55 302	39.53 358	52.55 357	82.71 324

ACMH+	65.35 16	67.65 304	64.55 308	76.96 298	84.59 260	57.10 312	88.08 266	80.79 347	58.59 335	53.00 344	81.09 294	26.63 363	92.95 247	46.51 331	61.69 324	80.82 342

pmmvs6		67.57 305	64.76 306	76.00 305	72.82 363	53.37 333	88.71 258	86.78 307	53.19 353	57.58 330	78.03 323	35.33 334	92.41 272	55.56 295	54.88 351	82.21 331

Anonymous20231206		67.53 306	65.78 297	72.79 328	74.95 354	47.59 360	88.23 265	87.32 299	61.75 317	58.07 325	77.29 328	37.79 319	87.29 338	42.91 345	63.71 305	83.48 311

Patchmtry		67.53 306	63.93 313	78.34 277	82.12 292	64.38 174	68.72 369	84.00 331	48.23 368	59.24 316	72.41 351	57.82 156	89.27 318	46.10 334	56.68 346	81.36 336

USDC		67.43 308	64.51 309	76.19 303	77.94 341	55.29 324	78.38 346	85.00 322	73.17 161	48.36 362	80.37 302	21.23 372	92.48 271	52.15 307	64.02 303	80.81 343

ADS-MVSNet2		66.90 309	63.44 316	77.26 293	88.06 191	60.70 265	68.01 372	75.56 360	57.57 336	64.48 281	69.87 361	38.68 305	84.10 352	40.87 354	67.89 271	86.97 254

CMPMVS	48.56 21	66.77 310	64.41 311	73.84 320	70.65 369	50.31 347	77.79 350	85.73 317	45.54 373	44.76 372	82.14 273	35.40 333	90.14 312	63.18 259	74.54 221	81.07 340
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 311	62.92 319	76.80 300	76.51 348	57.77 302	89.22 249	83.41 337	55.48 348	53.86 342	77.84 324	26.28 364	93.95 226	34.90 370	68.76 264	78.68 361

LTVRE_ROB	59.60 19	66.27 312	63.54 315	74.45 315	84.00 271	51.55 340	67.08 375	83.53 335	58.78 333	54.94 337	80.31 303	34.54 336	93.23 241	40.64 356	68.03 269	78.58 362
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		66.06 313	62.45 322	76.88 299	81.42 298	54.45 330	57.49 387	88.67 274	49.36 364	63.86 287	46.86 385	56.06 180	90.25 306	49.53 316	68.83 263	85.95 278

Patchmatch-test		65.86 314	60.94 328	80.62 236	83.75 273	58.83 292	58.91 386	75.26 362	44.50 376	50.95 354	77.09 331	58.81 148	87.90 328	35.13 369	64.03 302	95.12 72

UnsupCasMVSNet_eth		65.79 315	63.10 317	73.88 319	70.71 368	50.29 348	81.09 327	89.88 222	72.58 175	49.25 360	74.77 346	32.57 344	87.43 337	55.96 294	41.04 376	83.90 305

test_fmvs2		65.78 316	64.84 304	68.60 348	66.54 377	41.71 377	83.27 308	69.81 375	54.38 350	67.91 248	84.54 248	15.35 381	81.22 372	75.65 145	66.16 282	82.88 319

dmvs_testset		65.55 317	66.45 293	62.86 358	79.87 314	22.35 401	76.55 353	71.74 371	77.42 103	55.85 334	87.77 208	51.39 228	80.69 373	31.51 383	65.92 284	85.55 287

pmmvs-eth3d		65.53 318	62.32 323	75.19 309	69.39 373	59.59 280	82.80 315	83.43 336	62.52 308	51.30 352	72.49 349	32.86 341	87.16 339	55.32 296	50.73 360	78.83 360

SixPastTwentyTwo		64.92 319	61.78 326	74.34 317	78.74 331	49.76 349	83.42 307	79.51 353	62.86 304	50.27 355	77.35 326	30.92 353	90.49 304	45.89 335	47.06 366	82.78 320

OurMVSNet-221017-0		64.68 320	62.17 324	72.21 333	76.08 352	47.35 361	80.67 330	81.02 346	56.19 345	51.60 349	79.66 313	27.05 362	88.56 322	53.60 304	53.63 354	80.71 344

test_0402		64.54 321	61.09 327	74.92 312	84.10 270	60.75 262	87.95 270	79.71 352	52.03 355	52.41 346	77.20 329	32.21 346	91.64 290	23.14 386	61.03 327	72.36 375

testgi		64.48 322	62.87 320	69.31 345	71.24 364	40.62 380	85.49 292	79.92 351	65.36 284	54.18 340	83.49 259	23.74 368	84.55 350	41.60 351	60.79 330	82.77 321

RPSCF		64.24 323	61.98 325	71.01 340	76.10 351	45.00 370	75.83 357	75.94 357	46.94 370	58.96 320	84.59 246	31.40 349	82.00 369	47.76 327	60.33 335	86.04 275

EU-MVSNet		64.01 324	63.01 318	67.02 354	74.40 357	38.86 385	83.27 308	86.19 312	45.11 374	54.27 339	81.15 293	36.91 328	80.01 375	48.79 320	57.02 343	82.19 332

test20.03		63.83 325	62.65 321	67.38 353	70.58 370	39.94 381	86.57 289	84.17 328	63.29 299	51.86 348	77.30 327	37.09 326	82.47 365	38.87 362	54.13 353	79.73 352

MDA-MVSNet_test_wron		63.78 326	60.16 329	74.64 313	78.15 339	60.41 269	83.49 304	84.03 329	56.17 347	39.17 381	71.59 357	37.22 323	83.24 362	42.87 347	48.73 363	80.26 349

YYNet1		63.76 327	60.14 330	74.62 314	78.06 340	60.19 274	83.46 306	83.99 333	56.18 346	39.25 380	71.56 358	37.18 324	83.34 360	42.90 346	48.70 364	80.32 348

K. test v3		63.09 328	59.61 332	73.53 322	76.26 350	49.38 354	83.27 308	77.15 355	64.35 290	47.77 364	72.32 353	28.73 357	87.79 331	49.93 315	36.69 382	83.41 313

COLMAP_ROB	57.96 20	62.98 329	59.65 331	72.98 326	81.44 297	53.00 335	83.75 302	75.53 361	48.34 367	48.81 361	81.40 286	24.14 366	90.30 305	32.95 375	60.52 332	75.65 370
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 330	59.08 333	71.10 339	67.19 376	48.72 356	83.91 301	85.23 320	50.38 361	47.84 363	71.22 360	20.74 373	85.51 347	46.47 332	58.75 340	79.06 357

AllTest		61.66 331	58.06 335	72.46 330	79.57 316	51.42 342	80.17 336	68.61 377	51.25 358	45.88 366	81.23 288	19.86 377	86.58 341	38.98 360	57.01 344	79.39 354

UnsupCasMVSNet_bld		61.60 332	57.71 336	73.29 324	68.73 374	51.64 339	78.61 344	89.05 258	57.20 340	46.11 365	61.96 376	28.70 358	88.60 321	50.08 314	38.90 380	79.63 353

MDA-MVSNet-bldmvs		61.54 333	57.70 337	73.05 325	79.53 318	57.00 315	83.08 312	81.23 345	57.57 336	34.91 384	72.45 350	32.79 342	86.26 343	35.81 367	41.95 374	75.89 369

KD-MVS_self_test		60.87 334	58.60 334	67.68 351	66.13 378	39.93 382	75.63 358	84.70 324	57.32 339	49.57 358	68.45 364	29.55 354	82.87 363	48.09 322	47.94 365	80.25 350

TinyColmap		60.32 335	56.42 342	72.00 337	78.78 330	53.18 334	78.36 347	75.64 359	52.30 354	41.59 379	75.82 342	14.76 384	88.35 325	35.84 366	54.71 352	74.46 371

MVS-HIRNet		60.25 336	55.55 343	74.35 316	84.37 265	56.57 317	71.64 363	74.11 364	34.44 384	45.54 370	42.24 391	31.11 352	89.81 314	40.36 357	76.10 213	76.67 368

MIMVSNet1		60.16 337	57.33 338	68.67 347	69.71 371	44.13 372	78.92 343	84.21 327	55.05 349	44.63 373	71.85 355	23.91 367	81.54 371	32.63 378	55.03 350	80.35 347

PM-MVS		59.40 338	56.59 340	67.84 349	63.63 380	41.86 376	76.76 352	63.22 384	59.01 332	51.07 353	72.27 354	11.72 387	83.25 361	61.34 270	50.28 362	78.39 363

new-patchmatchnet		59.30 339	56.48 341	67.79 350	65.86 379	44.19 371	82.47 316	81.77 343	59.94 328	43.65 376	66.20 368	27.67 360	81.68 370	39.34 359	41.40 375	77.50 366

test_vis1_rt		59.09 340	57.31 339	64.43 356	68.44 375	46.02 368	83.05 313	48.63 396	51.96 356	49.57 358	63.86 372	16.30 379	80.20 374	71.21 182	62.79 309	67.07 381

test_fmvs3		56.82 341	54.86 344	62.69 359	53.59 390	35.47 387	75.87 356	65.64 382	43.91 377	55.10 336	71.43 359	6.91 395	74.40 380	68.64 209	52.63 355	78.20 364

DSMNet-mixed		56.78 342	54.44 345	63.79 357	63.21 381	29.44 396	64.43 378	64.10 383	42.12 381	51.32 351	71.60 356	31.76 347	75.04 378	36.23 365	65.20 290	86.87 257

pmmvs3		55.51 343	51.50 348	67.53 352	57.90 388	50.93 345	80.37 332	73.66 365	40.63 382	44.15 375	64.75 371	16.30 379	78.97 376	44.77 341	40.98 378	72.69 373

TDRefinement		55.28 344	51.58 347	66.39 355	59.53 387	46.15 367	76.23 355	72.80 367	44.60 375	42.49 377	76.28 338	15.29 382	82.39 366	33.20 374	43.75 371	70.62 377

LF4IMVS		54.01 345	52.12 346	59.69 360	62.41 383	39.91 383	68.59 370	68.28 379	42.96 380	44.55 374	75.18 343	14.09 386	68.39 386	41.36 353	51.68 358	70.78 376

N_pmnet		50.55 346	49.11 349	54.88 366	77.17 346	4.02 409	84.36 298	2.00 407	48.59 365	45.86 368	68.82 363	32.22 345	82.80 364	31.58 381	51.38 359	77.81 365

new_pmnet		49.31 347	46.44 350	57.93 361	62.84 382	40.74 379	68.47 371	62.96 385	36.48 383	35.09 383	57.81 380	14.97 383	72.18 382	32.86 376	46.44 367	60.88 383

mvsany_test3		48.86 348	46.35 351	56.41 362	46.00 396	31.67 392	62.26 380	47.25 397	43.71 378	45.54 370	68.15 365	10.84 388	64.44 394	57.95 286	35.44 385	73.13 372

test_f		46.58 349	43.45 353	55.96 363	45.18 397	32.05 391	61.18 381	49.49 395	33.39 385	42.05 378	62.48 375	7.00 394	65.56 390	47.08 330	43.21 373	70.27 378

WB-MVS		46.23 350	44.94 352	50.11 370	62.13 384	21.23 403	76.48 354	55.49 389	45.89 372	35.78 382	61.44 378	35.54 332	72.83 381	9.96 397	21.75 392	56.27 385

FPMVS		45.64 351	43.10 355	53.23 368	51.42 393	36.46 386	64.97 377	71.91 370	29.13 388	27.53 388	61.55 377	9.83 390	65.01 392	16.00 394	55.58 348	58.22 384

SSC-MVS		44.51 352	43.35 354	47.99 374	61.01 386	18.90 405	74.12 360	54.36 390	43.42 379	34.10 385	60.02 379	34.42 337	70.39 384	9.14 399	19.57 393	54.68 386

EGC-MVSNET		42.35 353	38.09 356	55.11 365	74.57 355	46.62 366	71.63 364	55.77 388	0.04 402	0.24 403	62.70 374	14.24 385	74.91 379	17.59 391	46.06 368	43.80 388

LCM-MVSNet		40.54 354	35.79 359	54.76 367	36.92 403	30.81 393	51.41 390	69.02 376	22.07 390	24.63 390	45.37 387	4.56 399	65.81 389	33.67 372	34.50 386	67.67 379

APD_test1		40.50 355	37.31 358	50.09 371	51.88 391	35.27 388	59.45 385	52.59 392	21.64 391	26.12 389	57.80 381	4.56 399	66.56 388	22.64 387	39.09 379	48.43 387

test_vis3_rt		40.46 356	37.79 357	48.47 373	44.49 398	33.35 390	66.56 376	32.84 404	32.39 386	29.65 386	39.13 394	3.91 402	68.65 385	50.17 312	40.99 377	43.40 389

ANet_high		40.27 357	35.20 360	55.47 364	34.74 404	34.47 389	63.84 379	71.56 372	48.42 366	18.80 393	41.08 392	9.52 391	64.45 393	20.18 389	8.66 400	67.49 380

test_method		38.59 358	35.16 361	48.89 372	54.33 389	21.35 402	45.32 393	53.71 391	7.41 399	28.74 387	51.62 383	8.70 392	52.87 397	33.73 371	32.89 387	72.47 374

PMMVS2		37.93 359	33.61 362	50.92 369	46.31 395	24.76 399	60.55 384	50.05 393	28.94 389	20.93 391	47.59 384	4.41 401	65.13 391	25.14 385	18.55 395	62.87 382

Gipuma		34.91 360	31.44 363	45.30 375	70.99 367	39.64 384	19.85 397	72.56 368	20.10 393	16.16 397	21.47 398	5.08 398	71.16 383	13.07 395	43.70 372	25.08 395
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 361	29.47 364	42.67 377	41.89 400	30.81 393	52.07 388	43.45 398	15.45 394	18.52 394	44.82 388	2.12 403	58.38 395	16.05 392	30.87 389	38.83 390

APD_test2		32.77 361	29.47 364	42.67 377	41.89 400	30.81 393	52.07 388	43.45 398	15.45 394	18.52 394	44.82 388	2.12 403	58.38 395	16.05 392	30.87 389	38.83 390

PMVS	26.43 22	31.84 363	28.16 366	42.89 376	25.87 406	27.58 397	50.92 391	49.78 394	21.37 392	14.17 398	40.81 393	2.01 405	66.62 387	9.61 398	38.88 381	34.49 394
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 364	24.00 368	26.45 381	43.74 399	18.44 406	60.86 382	39.66 400	15.11 396	9.53 400	22.10 397	6.52 396	46.94 399	8.31 400	10.14 397	13.98 397

MVE	24.84 23	24.35 365	19.77 371	38.09 379	34.56 405	26.92 398	26.57 395	38.87 402	11.73 398	11.37 399	27.44 395	1.37 406	50.42 398	11.41 396	14.60 396	36.93 392
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 366	23.20 370	25.46 382	41.52 402	16.90 407	60.56 383	38.79 403	14.62 397	8.99 401	20.24 400	7.35 393	45.82 400	7.25 401	9.46 398	13.64 398

tmp_tt		22.26 367	23.75 369	17.80 383	5.23 407	12.06 408	35.26 394	39.48 401	2.82 401	18.94 392	44.20 390	22.23 371	24.64 402	36.30 364	9.31 399	16.69 396

cdsmvs_eth3d_5k		19.86 368	26.47 367	0.00 387	0.00 410	0.00 412	0.00 398	93.45 80	0.00 405	0.00 406	95.27 56	49.56 243	0.00 406	0.00 405	0.00 403	0.00 402

wuyk23d		11.30 369	10.95 372	12.33 384	48.05 394	19.89 404	25.89 396	1.92 408	3.58 400	3.12 402	1.37 402	0.64 407	15.77 403	6.23 402	7.77 401	1.35 399

ab-mvs-re		7.91 370	10.55 373	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	94.95 64	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

testmvs		7.23 371	9.62 374	0.06 386	0.04 408	0.02 411	84.98 296	0.02 409	0.03 403	0.18 404	1.21 403	0.01 409	0.02 404	0.14 403	0.01 402	0.13 401

test123		6.92 372	9.21 375	0.08 385	0.03 409	0.05 410	81.65 322	0.01 410	0.02 404	0.14 405	0.85 404	0.03 408	0.02 404	0.12 404	0.00 403	0.16 400

pcd_1.5k_mvsjas		4.46 373	5.95 376	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	53.55 208	0.00 406	0.00 405	0.00 403	0.00 402

test_blank		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

uanet_test		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

DCPMVS		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

sosnet-low-res		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

sosnet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

uncertanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

Regformer		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

uanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 412	0.00 398	0.00 411	0.00 405	0.00 406	0.00 405	0.00 410	0.00 406	0.00 405	0.00 403	0.00 402

WAC-MVS						49.45 352								31.56 382

FOURS1					93.95 45	61.77 241	93.96 70	91.92 137	62.14 311	86.57 44

MSC_two_6792asdad				89.60 8	97.31 4	73.22 10		95.05 23					99.07 13	92.01 24	94.77 25	96.51 21

PC_three_1452									80.91 45	94.07 2	96.83 18	83.57 4	99.12 5	95.70 7	97.42 4	97.55 4

No_MVS				89.60 8	97.31 4	73.22 10		95.05 23					99.07 13	92.01 24	94.77 25	96.51 21

test_one_0601					96.32 18	69.74 43		94.18 53	71.42 218	90.67 18	96.85 16	74.45 18

eth-test2					0.00 410

eth-test					0.00 410

ZD-MVS					96.63 9	65.50 148		93.50 78	70.74 232	85.26 59	95.19 61	64.92 75	97.29 76	87.51 55	93.01 54

RE-MVS-def			80.48 133		92.02 94	58.56 295	90.90 202	90.45 195	62.76 305	78.89 117	94.46 78	49.30 246		78.77 127	86.77 123	92.28 171

IU-MVS					96.46 11	69.91 37		95.18 18	80.75 46	95.28 1				92.34 21	95.36 13	96.47 25

OPU-MVS				89.97 3	97.52 3	73.15 12	96.89 5				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

test_241102_TWO								94.41 44	71.65 207	92.07 8	97.21 4	74.58 17	99.11 6	92.34 21	95.36 13	96.59 16

test_241102_ONE					96.45 12	69.38 48		94.44 42	71.65 207	92.11 6	97.05 7	76.79 9	99.11 6

9.14			87.63 27		93.86 47		94.41 52	94.18 53	72.76 172	86.21 46	96.51 24	66.64 57	97.88 44	90.08 38	94.04 37

save fliter					93.84 48	67.89 87	95.05 39	92.66 110	78.19 86

test_0728_THIRD									72.48 177	90.55 19	96.93 11	76.24 11	99.08 11	91.53 29	94.99 17	96.43 26

test_0728_SECOND				88.70 16	96.45 12	70.43 29	96.64 9	94.37 48					99.15 2	91.91 27	94.90 21	96.51 21

test0726					96.40 15	69.99 33	96.76 7	94.33 50	71.92 193	91.89 10	97.11 6	73.77 21

GSMVS																94.68 89

test_part2					96.29 19	68.16 81				90.78 16

sam_mvs1												57.85 155				94.68 89

sam_mvs												54.91 193

ambc				69.61 343	61.38 385	41.35 378	49.07 392	85.86 316		50.18 357	66.40 367	10.16 389	88.14 327	45.73 336	44.20 370	79.32 356

MTGPA								92.23 123

test_post1							78.95 342				20.70 399	53.05 213	91.50 298	60.43 275

test_post											23.01 396	56.49 175	92.67 262

patchmatchnet-post											67.62 366	57.62 158	90.25 306

GG-mvs-BLEND				86.53 65	91.91 101	69.67 46	75.02 359	94.75 30		78.67 124	90.85 158	77.91 7	94.56 195	72.25 172	93.74 43	95.36 58

MTMP							93.77 84	32.52 405

gm-plane-assit					88.42 179	67.04 110			78.62 83		91.83 141		97.37 70	76.57 139

test9_res														89.41 39	94.96 18	95.29 63

TEST9					94.18 41	67.28 102	94.16 58	93.51 76	71.75 204	85.52 54	95.33 51	68.01 47	97.27 80

test_8					94.19 40	67.19 104	94.15 61	93.42 82	71.87 198	85.38 57	95.35 50	68.19 45	96.95 102

agg_prior2														86.41 66	94.75 29	95.33 59

agg_prior					94.16 43	66.97 112		93.31 85		84.49 65			96.75 111

TestCases				72.46 330	79.57 316	51.42 342		68.61 377	51.25 358	45.88 366	81.23 288	19.86 377	86.58 341	38.98 360	57.01 344	79.39 354

test_prior4						67.18 106	93.92 73

test_prior2							95.10 38		75.40 125	85.25 60	95.61 45	67.94 48		87.47 56	94.77 25

test_prior				86.42 68	94.71 35	67.35 101		93.10 95					96.84 108			95.05 74

旧先验2							92.00 156		59.37 331	87.54 38			93.47 238	75.39 147

新几何2							91.41 177

新几何1				84.73 124	92.32 86	64.28 179		91.46 162	59.56 330	79.77 106	92.90 118	56.95 168	96.57 116	63.40 255	92.91 56	93.34 139

旧先验1					91.94 98	60.74 263		91.50 160			94.36 82	65.23 70			91.84 69	94.55 96

无先验							92.71 121	92.61 114	62.03 312				97.01 93	66.63 226		93.97 121

原ACMM2							92.01 153

原ACMM1				84.42 138	93.21 63	64.27 180		93.40 84	65.39 283	79.51 109	92.50 126	58.11 154	96.69 112	65.27 245	93.96 38	92.32 169

test222					89.77 145	61.60 246	89.55 241	89.42 239	56.83 343	77.28 137	92.43 130	52.76 216			91.14 83	93.09 147

testdata2													96.09 129	61.26 271

segment_acmp												65.94 63

testdata				81.34 218	89.02 165	57.72 303		89.84 223	58.65 334	85.32 58	94.09 94	57.03 163	93.28 240	69.34 200	90.56 89	93.03 150

testdata1							89.21 250		77.55 99

test12				87.09 45	94.60 36	68.86 61		92.91 101		82.67 81		65.44 68	97.55 62		93.69 46	94.84 83

plane_prior7					86.94 219	61.51 247

plane_prior6					87.23 211	62.32 231						50.66 233

plane_prior5								91.31 166					95.55 158	76.74 137	78.53 191	88.39 234

plane_prior4											89.14 185

plane_prior3						61.95 239			79.09 73	72.53 187

plane_prior2							93.13 105		78.81 80

plane_prior1					87.15 213

plane_prior						62.42 227	93.85 77		79.38 65						78.80 188

n2								0.00 411

nn								0.00 411

door-mid								66.01 381

lessismore_v0				73.72 321	72.93 362	47.83 359		61.72 386		45.86 368	73.76 347	28.63 359	89.81 314	47.75 328	31.37 388	83.53 309

LGP-MVS_train				79.56 264	84.31 266	59.37 284		89.73 229	69.49 246	64.86 275	88.42 190	38.65 307	94.30 204	72.56 169	72.76 236	85.01 296

test11								93.01 97

door								66.57 380

HQP5-MVS						63.66 198

HQP-NCC					87.54 204		94.06 63		79.80 57	74.18 166

ACMP_Plane					87.54 204		94.06 63		79.80 57	74.18 166

BP-MVS														77.63 134

HQP4-MVS										74.18 166			95.61 153			88.63 227

HQP3-MVS								91.70 152							78.90 186

HQP2-MVS												51.63 226

NP-MVS					87.41 207	63.04 213					90.30 168

MDTV_nov1_ep13_2view						59.90 277	80.13 337		67.65 267	72.79 182		54.33 201		59.83 279		92.58 162

MDTV_nov1_ep13			72.61 249		89.06 164	68.48 69	80.33 333	90.11 213	71.84 200	71.81 198	75.92 341	53.01 214	93.92 227	48.04 323	73.38 230

ACMMP++_ref															71.63 244

ACMMP++															69.72 254

Test By Simon												54.21 202

ITE_SJBPF				70.43 341	74.44 356	47.06 365		77.32 354	60.16 326	54.04 341	83.53 257	23.30 369	84.01 354	43.07 344	61.58 325	80.21 351

DeepMVS_CX				34.71 380	51.45 392	24.73 400		28.48 406	31.46 387	17.49 396	52.75 382	5.80 397	42.60 401	18.18 390	19.42 394	36.81 393

High-resolution multi-view benchmark