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 49	72.48 178	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 51	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 47	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 43	71.65 208	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 45	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 63	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 39	71.92 194	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 41	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 36	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 56	68.77 258	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 59	74.18 141	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 87	83.86 14	89.55 29	96.06 36	53.55 209	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	90.36 221	93.55 76	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 62	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 57	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 95	83.53 17	89.55 29	95.95 38	53.45 213	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	89.24 162	71.18 19	96.57 11	92.90 103	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	87.05 218	63.55 202	93.69 87	91.08 180	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 63	69.35 249	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	86.95 219	64.37 175	94.30 54	88.45 281	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	92.63 114	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	87.10 216	64.19 182	94.41 52	88.14 290	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	90.36 221	90.66 192	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 75	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 77	71.87 199	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 41	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 66	70.78 232	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	68.48 262	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	91.71 151	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	71.77 204	85.69 53	96.52 23	62.07 113	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 61	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 73	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	85.28 249	62.55 226	94.26 56	89.78 225	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	86.16 234	70.54 28	95.71 24	92.19 129	82.00 30	84.58 64	94.34 87	61.86 115	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	87.36 211	63.54 203	94.74 47	90.02 219	82.52 24	90.14 24	96.92 13	62.93 106	97.84 46	95.28 8	82.26 157	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	82.25 158	96.54 19

MSLP-MVS++		86.27 44	85.91 51	87.35 39	92.01 96	68.97 60	95.04 40	92.70 108	79.04 76	81.50 87	96.50 25	58.98 148	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	58.22 153	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 66	79.08 74	78.88 120	93.99 97	62.25 112	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	91.92 138	80.09 54	85.46 56	95.53 47	61.82 118	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	91.79 165	93.49 80	74.93 132	84.61 63	95.30 53	59.42 142	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	91.92 138	81.21 42	84.13 70	94.07 96	60.93 126	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	92.58 116	66.54 276	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	88.20 190	72.42 13	92.41 137	92.77 106	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	91.86 143	79.97 55	84.82 62	95.40 49	62.26 111	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	85.73 243	63.58 200	93.79 83	89.32 243	81.42 39	90.21 22	96.91 14	62.41 110	97.67 51	94.48 10	80.56 174	92.90 155

test_fmvsmconf0.1_n		85.71 54	86.08 48	84.62 132	80.83 302	62.33 230	93.84 80	88.81 268	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	92.83 105	70.90 228	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	88.22 189	69.35 51	93.74 86	91.89 141	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	82.95 286	63.48 205	94.03 68	89.46 237	81.69 33	89.86 25	96.74 20	61.85 116	97.75 49	94.74 9	82.01 161	92.81 157

DeepC-MVS	77.85 3	85.52 58	85.24 58	86.37 70	88.80 172	66.64 119	92.15 144	93.68 71	81.07 43	76.91 142	93.64 104	62.59 108	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	88.25 187	69.07 56	93.04 109	91.76 148	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 84	73.75 152	79.94 104	94.68 74	60.61 129	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	91.54 175	92.51 118	74.56 135	80.62 97	95.64 44	59.15 146	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	91.85 163	93.00 100	76.59 113	79.03 116	95.00 63	61.59 119	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	92.27 123	73.13 163	79.63 108	94.43 80	61.90 114	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	88.33 184	68.73 64	90.24 226	91.82 147	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	92.32 121	81.87 31	75.68 151	88.27 197	60.18 132	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	92.04 133	79.45 63	79.33 113	94.28 90	62.42 109	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	80.23 312	63.50 204	92.79 117	88.73 272	80.46 49	89.84 26	96.65 22	60.96 125	97.57 61	93.80 13	80.14 176	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 91	72.19 188	79.22 114	94.93 66	59.04 147	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	87.91 272	95.54 12	68.55 260	72.35 194	94.71 73	59.78 138	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 94	73.20 161	78.89 117	94.18 93	59.41 143	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	91.09 178	77.91 91	83.24 74	94.20 92	58.37 151	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 93	72.19 188	78.85 122	94.86 69	56.69 173	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	92.95 101	72.16 191	78.86 121	94.84 70	56.97 168	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	91.89 141	71.90 196	82.16 83	93.49 108	47.98 260	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	69.19 252	81.09 91	92.88 120	57.00 166	97.44 66	81.11 109	81.76 163	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	69.19 252	81.09 91	92.88 120	57.00 166	97.44 66	81.11 109	81.76 163	96.23 33

diffmvs		84.28 76	83.83 74	85.61 93	87.40 209	68.02 84	90.88 205	89.24 246	80.54 47	81.64 86	92.52 125	59.83 137	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	84.65 298	94.50 40	79.15 71	82.23 82	87.93 206	66.88 55	96.94 103	80.53 112	82.20 159	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	82.76 155	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 53	74.41 136	79.16 115	95.61 45	53.99 204	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	89.99 235	92.05 132	77.77 93	82.84 77	86.57 225	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	91.87 161	89.01 260	85.27 9	86.09 48	93.74 101	47.71 264	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 37	81.44 38	58.19 324	93.64 104	73.64 23	92.35 276	82.66 94	78.66 191	96.50 24

PVSNet_Blended_VisFu		83.97 85	83.50 79	85.39 99	90.02 140	66.59 122	93.77 84	91.73 149	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	81.75 321	92.23 124	75.32 127	80.53 98	95.21 60	56.06 181	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	92.66 111	73.99 144	78.18 126	94.31 89	55.25 187	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	91.35 187	86.12 314	72.59 175	83.22 75	92.81 123	59.60 140	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	82.51 288	63.72 193	91.37 185	83.99 334	81.42 39	77.68 131	95.74 42	58.37 151	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	91.76 168	94.81 28	79.65 61	77.87 129	94.09 94	63.35 99	97.90 42	79.35 119	79.36 183	90.74 200

MVSFormer		83.75 91	82.88 96	86.37 70	89.24 162	71.18 19	89.07 254	90.69 189	65.80 281	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	92.28 122	71.03 226	77.41 135	94.92 67	55.21 190	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	75.26 354	61.72 244	92.17 143	87.24 303	82.36 26	84.91 61	95.41 48	55.60 185	96.83 109	92.85 17	85.87 131	94.21 108

baseline2		83.68 94	83.42 85	84.48 137	87.37 210	66.00 134	90.06 230	95.93 8	79.71 60	69.08 230	90.39 166	77.92 6	96.28 123	78.91 125	81.38 167	91.16 196

thisisatest0515		83.41 95	82.49 104	86.16 75	89.46 153	68.26 76	93.54 94	94.70 33	74.31 139	75.75 149	90.92 156	72.62 28	96.52 119	69.64 195	81.50 166	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 73	79.13 72	81.00 94	85.14 240	63.19 101	97.29 76	87.08 61	73.91 229	84.83 299

test2506		83.29 97	82.92 95	84.37 141	88.39 182	63.18 212	92.01 153	91.35 166	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	81.76 345	82.74 21	73.83 174	88.40 193	72.77 27	94.61 189	82.10 98	75.21 218	88.48 232

PGM-MVS		83.25 99	82.70 100	84.92 114	92.81 76	64.07 184	90.44 217	92.20 128	71.28 220	77.23 138	94.43 80	55.17 191	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	90.91 202	91.86 143	70.30 238	77.12 139	93.96 98	56.75 171	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	91.38 184	94.68 34	79.22 69	76.60 144	93.75 100	62.64 107	97.76 48	78.07 132	78.01 194	90.05 209

VDD-MVS		83.06 102	81.81 113	86.81 53	90.86 126	67.70 91	95.40 29	91.50 161	75.46 124	81.78 85	92.34 133	40.09 302	97.13 87	86.85 64	82.04 160	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 66	81.45 36	82.73 79	92.25 136	60.11 133	97.13 87	87.69 53	62.96 308	93.91 124

PAPM_NR		82.97 104	81.84 112	86.37 70	94.10 44	66.76 117	87.66 277	92.84 104	69.96 242	74.07 171	93.57 106	63.10 104	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	91.85 145	71.52 216	75.61 154	94.24 91	53.48 212	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	91.28 170	64.48 290	81.27 88	95.28 54	53.71 208	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	91.99 135	69.08 255	76.50 146	93.89 99	54.48 199	98.20 35	70.76 186	85.66 133	92.69 158

CLD-MVS		82.73 107	82.35 107	83.86 154	87.90 197	67.65 93	95.45 28	92.18 130	85.06 10	72.58 187	92.27 134	52.46 220	95.78 141	84.18 84	79.06 186	88.16 238
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	89.25 159	59.58 281	92.24 141	94.89 25	77.96 89	79.86 105	92.38 131	56.70 172	97.05 89	77.26 136	80.86 171	94.55 96

3Dnovator	73.91 6	82.69 110	80.82 126	88.31 23	89.57 149	71.26 18	92.60 129	94.39 48	78.84 79	67.89 251	92.48 129	48.42 255	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	91.90 160	93.21 88	79.83 56	72.14 195	85.71 237	74.72 16	94.72 184	75.72 144	72.49 240	87.50 243

TESTMET0.1,1		82.41 112	81.98 111	83.72 159	88.08 191	63.74 191	92.70 122	93.77 65	79.30 67	77.61 133	87.57 212	58.19 154	94.08 215	73.91 159	86.68 126	93.33 141

CostFormer		82.33 113	81.15 118	85.86 83	89.01 167	68.46 70	82.39 318	93.01 98	75.59 122	80.25 101	81.57 283	72.03 32	94.96 176	79.06 123	77.48 202	94.16 111

API-MVS		82.28 114	80.53 133	87.54 35	96.13 22	70.59 27	93.63 90	91.04 184	65.72 283	75.45 156	92.83 122	56.11 180	98.89 20	64.10 252	89.75 96	93.15 145

IB-MVS	77.80 4	82.18 115	80.46 135	87.35 39	89.14 164	70.28 31	95.59 26	95.17 19	78.85 78	70.19 218	85.82 235	70.66 35	97.67 51	72.19 175	66.52 281	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	86.42 227	68.72 65	92.59 131	90.44 199	73.12 164	84.20 67	94.36 82	38.04 316	95.73 145	84.12 85	86.81 120	91.33 189

xiu_mvs_v1_base		82.16 116	81.12 119	85.26 106	86.42 227	68.72 65	92.59 131	90.44 199	73.12 164	84.20 67	94.36 82	38.04 316	95.73 145	84.12 85	86.81 120	91.33 189

xiu_mvs_v1_base_debi		82.16 116	81.12 119	85.26 106	86.42 227	68.72 65	92.59 131	90.44 199	73.12 164	84.20 67	94.36 82	38.04 316	95.73 145	84.12 85	86.81 120	91.33 189

3Dnovator+	73.60 7	82.10 119	80.60 132	86.60 60	90.89 125	66.80 116	95.20 34	93.44 82	74.05 143	67.42 257	92.49 128	49.46 245	97.65 55	70.80 185	91.68 72	95.33 59

MVS_111021_LR		82.02 120	81.52 115	83.51 165	88.42 180	62.88 221	89.77 239	88.93 264	76.78 109	75.55 155	93.10 111	50.31 237	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	56.17 319	91.13 198	90.69 189	77.96 89	80.09 103	93.57 106	46.33 274	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	91.08 199	91.66 155	81.87 31	74.86 161	91.67 145	69.98 38	94.92 179	71.76 178	64.75 296	91.29 194

EPP-MVSNet		81.79 123	81.52 115	82.61 184	88.77 173	60.21 273	93.02 111	93.66 72	68.52 261	72.90 182	90.39 166	72.19 31	94.96 176	74.93 152	79.29 185	92.67 159

iter_conf_final		81.74 124	80.93 125	84.18 147	92.66 80	69.10 55	92.94 113	82.80 343	79.01 77	74.85 162	88.40 193	61.83 117	94.61 189	79.36 118	76.52 211	88.83 223

test_vis1_n_1920		81.66 125	82.01 110	80.64 235	82.24 291	55.09 327	94.76 46	86.87 305	81.67 34	84.40 66	94.63 75	38.17 313	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	91.39 182	91.01 185	63.35 299	79.72 107	94.62 76	51.82 223	96.14 127	79.71 115	87.93 109	92.89 156

ACMMP		81.49 127	80.67 129	83.93 153	91.71 106	62.90 220	92.13 145	92.22 127	71.79 203	71.68 202	93.49 108	50.32 236	96.96 101	78.47 129	84.22 146	91.93 181
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	80.74 127	83.52 163	86.26 231	64.45 169	92.09 148	90.65 193	75.83 120	73.95 173	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	79.99 140	85.46 96	90.39 134	68.40 71	86.88 288	90.61 194	74.41 136	70.31 217	84.67 246	63.79 89	92.32 277	73.13 161	85.70 132	95.67 46

ECVR-MVS		81.29 130	80.38 136	84.01 152	88.39 182	61.96 238	92.56 134	86.79 307	77.66 96	76.63 143	91.42 149	46.34 273	95.24 169	74.36 157	89.23 97	94.85 80

thisisatest0530		81.15 131	80.07 137	84.39 140	88.26 186	65.63 143	91.40 180	94.62 37	71.27 221	70.93 208	89.18 184	72.47 29	96.04 134	65.62 241	76.89 208	91.49 185

Fast-Effi-MVS+		81.14 132	80.01 139	84.51 136	90.24 136	65.86 138	94.12 62	89.15 252	73.81 151	75.37 157	88.26 198	57.26 161	94.53 197	66.97 226	84.92 136	93.15 145

HQP-MVS		81.14 132	80.64 130	82.64 183	87.54 205	63.66 198	94.06 63	91.70 153	79.80 57	74.18 167	90.30 168	51.63 227	95.61 153	77.63 134	78.90 187	88.63 228

hse-mvs2		81.12 134	81.11 122	81.16 222	86.52 226	57.48 308	89.40 247	91.16 173	81.45 36	82.73 79	90.49 164	60.11 133	94.58 191	87.69 53	60.41 335	91.41 188

SR-MVS-dyc-post		81.06 135	80.70 128	82.15 199	92.02 94	58.56 295	90.90 203	90.45 196	62.76 306	78.89 117	94.46 78	51.26 231	95.61 153	78.77 127	86.77 123	92.28 171

HyFIR lowres test		81.03 136	79.56 147	85.43 97	87.81 201	68.11 82	90.18 227	90.01 220	70.65 234	72.95 181	86.06 233	63.61 94	94.50 199	75.01 151	79.75 180	93.67 131

nrg030		80.93 137	79.86 142	84.13 149	83.69 275	68.83 62	93.23 103	91.20 171	75.55 123	75.06 159	88.22 201	63.04 105	94.74 183	81.88 100	66.88 278	88.82 226

Vis-MVSNet		80.92 138	79.98 141	83.74 156	88.48 177	61.80 240	93.44 98	88.26 289	73.96 147	77.73 130	91.76 142	49.94 241	94.76 181	65.84 238	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	80.02 138	83.33 169	87.87 198	60.76 261	92.62 127	86.86 306	77.86 92	75.73 150	91.39 151	46.35 272	94.70 187	72.79 166	88.68 103	94.52 100

UWE-MVS		80.81 140	81.01 124	80.20 245	89.33 156	57.05 313	91.91 159	94.71 32	75.67 121	75.01 160	89.37 182	63.13 103	91.44 299	67.19 223	82.80 154	92.12 179

1314		80.70 141	78.95 158	85.94 80	87.77 203	67.56 95	87.91 272	92.55 117	72.17 190	67.44 256	93.09 112	50.27 238	97.04 92	71.68 180	87.64 112	93.23 143

tpmrst		80.57 142	79.14 157	84.84 117	90.10 139	68.28 75	81.70 322	89.72 232	77.63 98	75.96 148	79.54 315	64.94 74	92.71 259	75.43 146	77.28 205	93.55 134

1112_ss		80.56 143	79.83 143	82.77 179	88.65 174	60.78 259	92.29 139	88.36 283	72.58 176	72.46 191	94.95 64	65.09 71	93.42 239	66.38 232	77.71 196	94.10 114

VDDNet		80.50 144	78.26 166	87.21 41	86.19 232	69.79 41	94.48 50	91.31 167	60.42 324	79.34 112	90.91 157	38.48 311	96.56 117	82.16 97	81.05 169	95.27 66

BH-w/o		80.49 145	79.30 154	84.05 151	90.83 127	64.36 177	93.60 91	89.42 240	74.35 138	69.09 229	90.15 173	55.23 189	95.61 153	64.61 249	86.43 129	92.17 177

test_cas_vis1_n_1920		80.45 146	80.61 131	79.97 254	78.25 338	57.01 315	94.04 67	88.33 284	79.06 75	82.81 78	93.70 102	38.65 308	91.63 291	90.82 35	79.81 178	91.27 195

TAMVS		80.37 147	79.45 150	83.13 174	85.14 252	63.37 206	91.23 193	90.76 188	74.81 134	72.65 185	88.49 190	60.63 128	92.95 247	69.41 199	81.95 162	93.08 148

HQP_MVS		80.34 148	79.75 144	82.12 201	86.94 220	62.42 227	93.13 105	91.31 167	78.81 80	72.53 188	89.14 186	50.66 234	95.55 158	76.74 137	78.53 192	88.39 235

SDMVSNet		80.26 149	78.88 159	84.40 139	89.25 159	67.63 94	85.35 294	93.02 97	76.77 110	70.84 209	87.12 219	47.95 261	96.09 129	85.04 76	74.55 220	89.48 219

HPM-MVS_fast		80.25 150	79.55 149	82.33 191	91.55 111	59.95 276	91.32 189	89.16 251	65.23 287	74.71 164	93.07 114	47.81 263	95.74 144	74.87 155	88.23 105	91.31 193

ab-mvs		80.18 151	78.31 165	85.80 86	88.44 179	65.49 149	83.00 315	92.67 110	71.82 202	77.36 136	85.01 241	54.50 196	96.59 114	76.35 141	75.63 216	95.32 61

IS-MVSNet		80.14 152	79.41 151	82.33 191	87.91 196	60.08 275	91.97 157	88.27 287	72.90 171	71.44 205	91.73 144	61.44 120	93.66 234	62.47 266	86.53 127	93.24 142

test-LLR		80.10 153	79.56 147	81.72 210	86.93 222	61.17 251	92.70 122	91.54 158	71.51 217	75.62 152	86.94 221	53.83 205	92.38 273	72.21 173	84.76 139	91.60 183

PVSNet	73.49 8	80.05 154	78.63 161	84.31 143	90.92 124	64.97 160	92.47 135	91.05 183	79.18 70	72.43 192	90.51 163	37.05 328	94.06 217	68.06 212	86.00 130	93.90 126

UA-Net		80.02 155	79.65 145	81.11 224	89.33 156	57.72 303	86.33 291	89.00 263	77.44 101	81.01 93	89.15 185	59.33 144	95.90 138	61.01 273	84.28 144	89.73 215

test-mter		79.96 156	79.38 153	81.72 210	86.93 222	61.17 251	92.70 122	91.54 158	73.85 149	75.62 152	86.94 221	49.84 243	92.38 273	72.21 173	84.76 139	91.60 183

QAPM		79.95 157	77.39 183	87.64 30	89.63 148	71.41 17	93.30 101	93.70 70	65.34 286	67.39 259	91.75 143	47.83 262	98.96 16	57.71 289	89.81 93	92.54 163

UGNet		79.87 158	78.68 160	83.45 168	89.96 141	61.51 247	92.13 145	90.79 187	76.83 108	78.85 122	86.33 229	38.16 314	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		79.80 159	77.95 172	85.34 102	88.28 185	68.26 76	81.56 324	91.42 164	70.11 240	77.59 134	80.50 301	67.40 52	94.26 208	67.34 220	77.35 203	93.51 135

thres200		79.66 160	78.33 164	83.66 162	92.54 84	65.82 140	93.06 107	96.31 3	74.90 133	73.30 178	88.66 188	59.67 139	95.61 153	47.84 327	78.67 190	89.56 218

CPTT-MVS		79.59 161	79.16 156	80.89 233	91.54 112	59.80 278	92.10 147	88.54 280	60.42 324	72.96 180	93.28 110	48.27 256	92.80 256	78.89 126	86.50 128	90.06 208

Test_1112_low_res		79.56 162	78.60 162	82.43 187	88.24 188	60.39 270	92.09 148	87.99 294	72.10 192	71.84 198	87.42 214	64.62 79	93.04 243	65.80 239	77.30 204	93.85 128

tttt0517		79.50 163	78.53 163	82.41 190	87.22 213	61.43 249	89.75 240	94.76 29	69.29 250	67.91 249	88.06 205	72.92 25	95.63 151	62.91 262	73.90 230	90.16 207

FIs		79.47 164	79.41 151	79.67 261	85.95 237	59.40 283	91.68 172	93.94 60	78.06 88	68.96 234	88.28 196	66.61 58	91.77 288	66.20 235	74.99 219	87.82 240

BH-RMVSNet		79.46 165	77.65 175	84.89 115	91.68 107	65.66 141	93.55 93	88.09 292	72.93 168	73.37 177	91.12 155	46.20 276	96.12 128	56.28 294	85.61 134	92.91 154

PCF-MVS	73.15 9	79.29 166	77.63 176	84.29 144	86.06 235	65.96 136	87.03 284	91.10 177	69.86 244	69.79 225	90.64 159	57.54 160	96.59 114	64.37 251	82.29 156	90.32 205
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 167	79.57 146	78.24 282	88.46 178	52.29 338	90.41 219	89.12 254	74.24 140	69.13 228	91.91 140	65.77 65	90.09 314	59.00 285	88.09 107	92.33 168

114514_t		79.17 168	77.67 174	83.68 160	95.32 29	65.53 147	92.85 116	91.60 157	63.49 297	67.92 248	90.63 161	46.65 269	95.72 149	67.01 225	83.54 147	89.79 213

FA-MVS(test-final)		79.12 169	77.23 185	84.81 121	90.54 130	63.98 186	81.35 327	91.71 151	71.09 225	74.85 162	82.94 264	52.85 216	97.05 89	67.97 213	81.73 165	93.41 137

VPA-MVSNet		79.03 170	78.00 170	82.11 204	85.95 237	64.48 168	93.22 104	94.66 35	75.05 131	74.04 172	84.95 242	52.17 222	93.52 236	74.90 154	67.04 277	88.32 237

OPM-MVS		79.00 171	78.09 168	81.73 209	83.52 278	63.83 188	91.64 174	90.30 206	76.36 116	71.97 197	89.93 177	46.30 275	95.17 171	75.10 149	77.70 197	86.19 271
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 172	78.22 167	81.25 219	85.33 247	62.73 224	89.53 244	93.21 88	72.39 183	72.14 195	90.13 174	60.99 123	94.72 184	67.73 217	72.49 240	86.29 267

AdaColmap		78.94 173	77.00 189	84.76 123	96.34 17	65.86 138	92.66 126	87.97 296	62.18 311	70.56 211	92.37 132	43.53 290	97.35 72	64.50 250	82.86 151	91.05 198

GeoE		78.90 174	77.43 179	83.29 170	88.95 168	62.02 236	92.31 138	86.23 312	70.24 239	71.34 206	89.27 183	54.43 200	94.04 220	63.31 258	80.81 173	93.81 129

miper_enhance_ethall		78.86 175	77.97 171	81.54 214	88.00 195	65.17 154	91.41 178	89.15 252	75.19 129	68.79 237	83.98 255	67.17 53	92.82 254	72.73 167	65.30 287	86.62 264

VPNet		78.82 176	77.53 178	82.70 181	84.52 262	66.44 124	93.93 72	92.23 124	80.46 49	72.60 186	88.38 195	49.18 249	93.13 242	72.47 171	63.97 305	88.55 231

EPNet_dtu		78.80 177	79.26 155	77.43 290	88.06 192	49.71 351	91.96 158	91.95 137	77.67 95	76.56 145	91.28 153	58.51 150	90.20 312	56.37 293	80.95 170	92.39 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 178	77.43 179	82.88 177	92.21 90	64.49 166	92.05 151	96.28 4	73.48 158	71.75 200	88.26 198	60.07 135	95.32 165	45.16 338	77.58 199	88.83 223

TR-MVS		78.77 179	77.37 184	82.95 176	90.49 131	60.88 257	93.67 88	90.07 215	70.08 241	74.51 165	91.37 152	45.69 279	95.70 150	60.12 279	80.32 175	92.29 170

thres400		78.68 180	77.43 179	82.43 187	92.21 90	64.49 166	92.05 151	96.28 4	73.48 158	71.75 200	88.26 198	60.07 135	95.32 165	45.16 338	77.58 199	87.48 244

BH-untuned		78.68 180	77.08 186	83.48 167	89.84 143	63.74 191	92.70 122	88.59 278	71.57 214	66.83 266	88.65 189	51.75 225	95.39 163	59.03 284	84.77 138	91.32 192

OMC-MVS		78.67 182	77.91 173	80.95 231	85.76 242	57.40 310	88.49 263	88.67 275	73.85 149	72.43 192	92.10 137	49.29 248	94.55 196	72.73 167	77.89 195	90.91 199

tpm		78.58 183	77.03 187	83.22 172	85.94 239	64.56 164	83.21 312	91.14 176	78.31 85	73.67 175	79.68 313	64.01 85	92.09 282	66.07 236	71.26 250	93.03 150

OpenMVS	70.45 11	78.54 184	75.92 203	86.41 69	85.93 240	71.68 16	92.74 119	92.51 118	66.49 277	64.56 281	91.96 139	43.88 289	98.10 37	54.61 299	90.65 87	89.44 221

EPMVS		78.49 185	75.98 202	86.02 77	91.21 119	69.68 45	80.23 336	91.20 171	75.25 128	72.48 190	78.11 323	54.65 195	93.69 233	57.66 290	83.04 150	94.69 88

AUN-MVS		78.37 186	77.43 179	81.17 221	86.60 225	57.45 309	89.46 246	91.16 173	74.11 142	74.40 166	90.49 164	55.52 186	94.57 193	74.73 156	60.43 334	91.48 186

thres100view900		78.37 186	77.01 188	82.46 186	91.89 102	63.21 210	91.19 197	96.33 1	72.28 186	70.45 214	87.89 207	60.31 130	95.32 165	45.16 338	77.58 199	88.83 223

GA-MVS		78.33 188	76.23 198	84.65 129	83.65 276	66.30 128	91.44 176	90.14 213	76.01 118	70.32 216	84.02 254	42.50 294	94.72 184	70.98 183	77.00 207	92.94 153

cascas		78.18 189	75.77 205	85.41 98	87.14 215	69.11 54	92.96 112	91.15 175	66.71 275	70.47 212	86.07 232	37.49 322	96.48 120	70.15 191	79.80 179	90.65 201

UniMVSNet_NR-MVSNet		78.15 190	77.55 177	79.98 252	84.46 264	60.26 271	92.25 140	93.20 90	77.50 100	68.88 235	86.61 224	66.10 61	92.13 280	66.38 232	62.55 312	87.54 242

thres600view7		78.00 191	76.66 193	82.03 206	91.93 99	63.69 196	91.30 190	96.33 1	72.43 181	70.46 213	87.89 207	60.31 130	94.92 179	42.64 350	76.64 209	87.48 244

FC-MVSNet-test		77.99 192	78.08 169	77.70 285	84.89 257	55.51 324	90.27 224	93.75 69	76.87 105	66.80 267	87.59 211	65.71 66	90.23 311	62.89 263	73.94 228	87.37 247

Anonymous202405211		77.96 193	75.33 212	85.87 82	93.73 52	64.52 165	94.85 44	85.36 320	62.52 309	76.11 147	90.18 171	29.43 357	97.29 76	68.51 210	77.24 206	95.81 45

cl22		77.94 194	76.78 191	81.42 216	87.57 204	64.93 162	90.67 212	88.86 267	72.45 180	67.63 255	82.68 268	64.07 84	92.91 252	71.79 176	65.30 287	86.44 265

XXY-MVS		77.94 194	76.44 195	82.43 187	82.60 287	64.44 170	92.01 153	91.83 146	73.59 157	70.00 221	85.82 235	54.43 200	94.76 181	69.63 196	68.02 271	88.10 239

MS-PatchMatch		77.90 196	76.50 194	82.12 201	85.99 236	69.95 36	91.75 170	92.70 108	73.97 146	62.58 302	84.44 250	41.11 299	95.78 141	63.76 255	92.17 64	80.62 346

FMVSNet3		77.73 197	76.04 201	82.80 178	91.20 120	68.99 59	91.87 161	91.99 135	73.35 160	67.04 262	83.19 263	56.62 174	92.14 279	59.80 281	69.34 258	87.28 251

miper_ehance_all_eth		77.60 198	76.44 195	81.09 228	85.70 244	64.41 173	90.65 213	88.64 277	72.31 184	67.37 260	82.52 269	64.77 78	92.64 266	70.67 187	65.30 287	86.24 269

UniMVSNet (Re)		77.58 199	76.78 191	79.98 252	84.11 270	60.80 258	91.76 168	93.17 92	76.56 114	69.93 224	84.78 245	63.32 100	92.36 275	64.89 248	62.51 314	86.78 259

PatchmatchNet		77.46 200	74.63 218	85.96 79	89.55 151	70.35 30	79.97 341	89.55 235	72.23 187	70.94 207	76.91 334	57.03 164	92.79 257	54.27 301	81.17 168	94.74 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 201	75.65 208	82.73 180	80.38 308	67.13 107	91.85 163	90.23 210	75.09 130	69.37 226	83.39 261	53.79 207	94.44 200	71.77 177	65.00 293	86.63 263

CHOSEN 280x420		77.35 202	76.95 190	78.55 277	87.07 217	62.68 225	69.71 369	82.95 341	68.80 257	71.48 204	87.27 218	66.03 62	84.00 356	76.47 140	82.81 153	88.95 222

PS-MVSNAJss		77.26 203	76.31 197	80.13 247	80.64 306	59.16 288	90.63 216	91.06 182	72.80 172	68.58 241	84.57 248	53.55 209	93.96 225	72.97 162	71.96 244	87.27 252

gg-mvs-nofinetune		77.18 204	74.31 225	85.80 86	91.42 114	68.36 72	71.78 363	94.72 31	49.61 364	77.12 139	45.92 387	77.41 8	93.98 224	67.62 218	93.16 53	95.05 74

WB-MVSnew		77.14 205	76.18 200	80.01 251	86.18 233	63.24 209	91.26 191	94.11 57	71.72 206	73.52 176	87.29 217	45.14 284	93.00 245	56.98 291	79.42 181	83.80 307

MVP-Stereo		77.12 206	76.23 198	79.79 259	81.72 296	66.34 127	89.29 248	90.88 186	70.56 236	62.01 305	82.88 265	49.34 246	94.13 212	65.55 243	93.80 41	78.88 360
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 207	75.37 210	82.20 197	89.25 159	62.11 235	82.06 319	89.09 256	76.77 110	70.84 209	87.12 219	41.43 298	95.01 174	67.23 222	74.55 220	89.48 219

dmvs_re		76.93 208	75.36 211	81.61 212	87.78 202	60.71 264	80.00 340	87.99 294	79.42 64	69.02 232	89.47 181	46.77 267	94.32 202	63.38 257	74.45 223	89.81 212

X-MVStestdata		76.86 209	74.13 229	85.05 110	93.22 61	63.78 189	92.92 114	92.66 111	73.99 144	78.18 126	10.19 402	55.25 187	97.41 68	79.16 121	91.58 74	93.95 122

DU-MVS		76.86 209	75.84 204	79.91 255	82.96 284	60.26 271	91.26 191	91.54 158	76.46 115	68.88 235	86.35 227	56.16 178	92.13 280	66.38 232	62.55 312	87.35 249

mvsmamba		76.85 211	75.71 207	80.25 243	83.07 283	59.16 288	91.44 176	80.64 350	76.84 107	67.95 247	86.33 229	46.17 277	94.24 209	76.06 142	72.92 236	87.36 248

Anonymous20240529		76.84 212	74.15 228	84.88 116	91.02 121	64.95 161	93.84 80	91.09 178	53.57 353	73.00 179	87.42 214	35.91 332	97.32 74	69.14 204	72.41 242	92.36 167

c3_l		76.83 213	75.47 209	80.93 232	85.02 255	64.18 183	90.39 220	88.11 291	71.66 207	66.65 268	81.64 281	63.58 96	92.56 267	69.31 201	62.86 309	86.04 276

WR-MVS		76.76 214	75.74 206	79.82 258	84.60 260	62.27 233	92.60 129	92.51 118	76.06 117	67.87 252	85.34 238	56.76 170	90.24 310	62.20 267	63.69 307	86.94 257

v1144		76.73 215	74.88 215	82.27 193	80.23 312	66.60 121	91.68 172	90.21 212	73.69 154	69.06 231	81.89 276	52.73 218	94.40 201	69.21 202	65.23 290	85.80 282

IterMVS-LS		76.49 216	75.18 214	80.43 238	84.49 263	62.74 223	90.64 214	88.80 269	72.40 182	65.16 275	81.72 279	60.98 124	92.27 278	67.74 216	64.65 298	86.29 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 217	74.55 221	82.19 198	79.14 326	67.82 88	90.26 225	89.42 240	73.75 152	68.63 240	81.89 276	51.31 230	94.09 214	71.69 179	64.84 294	84.66 300

v148		76.19 218	74.47 223	81.36 217	80.05 314	64.44 170	91.75 170	90.23 210	73.68 155	67.13 261	80.84 296	55.92 183	93.86 231	68.95 206	61.73 323	85.76 285

Effi-MVS+-dtu		76.14 219	75.28 213	78.72 276	83.22 280	55.17 326	89.87 236	87.78 297	75.42 125	67.98 246	81.43 285	45.08 285	92.52 269	75.08 150	71.63 245	88.48 232

cl____		76.07 220	74.67 216	80.28 241	85.15 251	61.76 242	90.12 228	88.73 272	71.16 222	65.43 272	81.57 283	61.15 121	92.95 247	66.54 229	62.17 316	86.13 274

DIV-MVS_self_test		76.07 220	74.67 216	80.28 241	85.14 252	61.75 243	90.12 228	88.73 272	71.16 222	65.42 273	81.60 282	61.15 121	92.94 251	66.54 229	62.16 318	86.14 272

FMVSNet2		76.07 220	74.01 231	82.26 195	88.85 169	67.66 92	91.33 188	91.61 156	70.84 229	65.98 269	82.25 272	48.03 257	92.00 284	58.46 286	68.73 266	87.10 254

v144192		76.05 223	74.03 230	82.12 201	79.50 320	66.55 123	91.39 182	89.71 233	72.30 185	68.17 244	81.33 288	51.75 225	94.03 222	67.94 214	64.19 300	85.77 283

NR-MVSNet		76.05 223	74.59 219	80.44 237	82.96 284	62.18 234	90.83 207	91.73 149	77.12 104	60.96 309	86.35 227	59.28 145	91.80 287	60.74 274	61.34 327	87.35 249

v1192		75.98 225	73.92 232	82.15 199	79.73 316	66.24 130	91.22 194	89.75 227	72.67 174	68.49 242	81.42 286	49.86 242	94.27 206	67.08 224	65.02 292	85.95 279

FE-MVS		75.97 226	73.02 242	84.82 118	89.78 144	65.56 145	77.44 352	91.07 181	64.55 289	72.66 184	79.85 311	46.05 278	96.69 112	54.97 298	80.82 172	92.21 176

eth_miper_zixun_eth		75.96 227	74.40 224	80.66 234	84.66 259	63.02 214	89.28 249	88.27 287	71.88 198	65.73 270	81.65 280	59.45 141	92.81 255	68.13 211	60.53 332	86.14 272

TranMVSNet+NR-MVSNet		75.86 228	74.52 222	79.89 256	82.44 289	60.64 267	91.37 185	91.37 165	76.63 112	67.65 254	86.21 231	52.37 221	91.55 293	61.84 269	60.81 330	87.48 244

SCA		75.82 229	72.76 246	85.01 112	86.63 224	70.08 32	81.06 329	89.19 249	71.60 213	70.01 220	77.09 332	45.53 280	90.25 307	60.43 276	73.27 232	94.68 89

LPG-MVS_test		75.82 229	74.58 220	79.56 265	84.31 267	59.37 284	90.44 217	89.73 230	69.49 247	64.86 276	88.42 191	38.65 308	94.30 204	72.56 169	72.76 237	85.01 297

GBi-Net		75.65 231	73.83 233	81.10 225	88.85 169	65.11 156	90.01 232	90.32 202	70.84 229	67.04 262	80.25 306	48.03 257	91.54 294	59.80 281	69.34 258	86.64 260

test1		75.65 231	73.83 233	81.10 225	88.85 169	65.11 156	90.01 232	90.32 202	70.84 229	67.04 262	80.25 306	48.03 257	91.54 294	59.80 281	69.34 258	86.64 260

v1921920		75.63 233	73.49 238	82.06 205	79.38 321	66.35 126	91.07 201	89.48 236	71.98 193	67.99 245	81.22 291	49.16 251	93.90 228	66.56 228	64.56 299	85.92 281

ACMP	71.68 10	75.58 234	74.23 227	79.62 263	84.97 256	59.64 279	90.80 208	89.07 258	70.39 237	62.95 298	87.30 216	38.28 312	93.87 229	72.89 163	71.45 248	85.36 292
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 235	73.26 240	81.61 212	80.67 305	66.82 114	89.54 243	89.27 245	71.65 208	63.30 294	80.30 305	54.99 193	94.06 217	67.33 221	62.33 315	83.94 305

tpm cat1		75.30 236	72.21 255	84.58 133	88.52 175	67.77 89	78.16 350	88.02 293	61.88 316	68.45 243	76.37 338	60.65 127	94.03 222	53.77 304	74.11 226	91.93 181

PLC	68.80 14	75.23 237	73.68 236	79.86 257	92.93 70	58.68 294	90.64 214	88.30 285	60.90 321	64.43 285	90.53 162	42.38 295	94.57 193	56.52 292	76.54 210	86.33 266
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 238	72.98 243	81.88 207	79.20 323	66.00 134	90.75 210	89.11 255	71.63 212	67.41 258	81.22 291	47.36 265	93.87 229	65.46 244	64.72 297	85.77 283

Fast-Effi-MVS+-dtu		75.04 239	73.37 239	80.07 248	80.86 301	59.52 282	91.20 196	85.38 319	71.90 196	65.20 274	84.84 244	41.46 297	92.97 246	66.50 231	72.96 235	87.73 241

dp		75.01 240	72.09 256	83.76 155	89.28 158	66.22 131	79.96 342	89.75 227	71.16 222	67.80 253	77.19 331	51.81 224	92.54 268	50.39 312	71.44 249	92.51 165

TAPA-MVS	70.22 12	74.94 241	73.53 237	79.17 270	90.40 133	52.07 339	89.19 252	89.61 234	62.69 308	70.07 219	92.67 124	48.89 254	94.32 202	38.26 364	79.97 177	91.12 197
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 242	72.54 252	81.46 215	80.33 310	66.71 118	89.15 253	89.08 257	70.94 227	63.08 297	79.86 310	52.52 219	94.04 220	65.70 240	62.17 316	83.64 308

XVG-OURS-SEG-HR		74.70 243	73.08 241	79.57 264	78.25 338	57.33 311	80.49 332	87.32 300	63.22 301	68.76 238	90.12 176	44.89 286	91.59 292	70.55 189	74.09 227	89.79 213

RRT_MVS		74.44 244	72.97 244	78.84 275	82.36 290	57.66 305	89.83 238	88.79 271	70.61 235	64.58 280	84.89 243	39.24 304	92.65 265	70.11 192	66.34 282	86.21 270

ACMM	69.62 13	74.34 245	72.73 248	79.17 270	84.25 269	57.87 301	90.36 221	89.93 221	63.17 303	65.64 271	86.04 234	37.79 320	94.10 213	65.89 237	71.52 247	85.55 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 246	72.30 254	80.32 239	91.49 113	61.66 245	90.85 206	80.72 349	56.67 345	63.85 289	90.64 159	46.75 268	90.84 302	53.79 303	75.99 215	88.47 234

XVG-OURS		74.25 247	72.46 253	79.63 262	78.45 336	57.59 307	80.33 334	87.39 299	63.86 294	68.76 238	89.62 180	40.50 301	91.72 289	69.00 205	74.25 225	89.58 216

test_fmvs1		74.07 248	73.69 235	75.22 309	78.91 330	47.34 363	89.06 256	74.69 364	63.68 296	79.41 111	91.59 147	24.36 366	87.77 333	85.22 74	76.26 213	90.55 204

CVMVSNet		74.04 249	74.27 226	73.33 324	85.33 247	43.94 374	89.53 244	88.39 282	54.33 352	70.37 215	90.13 174	49.17 250	84.05 354	61.83 270	79.36 183	91.99 180

Baseline_NR-MVSNet		73.99 250	72.83 245	77.48 289	80.78 303	59.29 287	91.79 165	84.55 327	68.85 256	68.99 233	80.70 297	56.16 178	92.04 283	62.67 264	60.98 329	81.11 340

pmmvs4		73.92 251	71.81 260	80.25 243	79.17 324	65.24 152	87.43 280	87.26 302	67.64 269	63.46 292	83.91 256	48.96 253	91.53 297	62.94 261	65.49 286	83.96 304

D2MVS		73.80 252	72.02 257	79.15 272	79.15 325	62.97 215	88.58 262	90.07 215	72.94 167	59.22 318	78.30 320	42.31 296	92.70 261	65.59 242	72.00 243	81.79 335

CR-MVSNet		73.79 253	70.82 268	82.70 181	83.15 281	67.96 85	70.25 366	84.00 332	73.67 156	69.97 222	72.41 352	57.82 157	89.48 318	52.99 307	73.13 233	90.64 202

test_djsdf		73.76 254	72.56 251	77.39 291	77.00 348	53.93 332	89.07 254	90.69 189	65.80 281	63.92 287	82.03 275	43.14 293	92.67 262	72.83 164	68.53 267	85.57 287

pmmvs5		73.35 255	71.52 262	78.86 274	78.64 334	60.61 268	91.08 199	86.90 304	67.69 266	63.32 293	83.64 257	44.33 288	90.53 304	62.04 268	66.02 284	85.46 290

Anonymous20231211		73.08 256	70.39 272	81.13 223	90.62 129	63.33 207	91.40 180	90.06 217	51.84 358	64.46 284	80.67 299	36.49 330	94.07 216	63.83 254	64.17 301	85.98 278

tt0805		73.07 257	70.73 269	80.07 248	78.37 337	57.05 313	87.78 274	92.18 130	61.23 320	67.04 262	86.49 226	31.35 351	94.58 191	65.06 247	67.12 276	88.57 230

miper_lstm_enhance		73.05 258	71.73 261	77.03 296	83.80 273	58.32 297	81.76 320	88.88 265	69.80 245	61.01 308	78.23 322	57.19 162	87.51 337	65.34 245	59.53 337	85.27 295

jajsoiax		73.05 258	71.51 263	77.67 286	77.46 345	54.83 328	88.81 258	90.04 218	69.13 254	62.85 300	83.51 259	31.16 352	92.75 258	70.83 184	69.80 254	85.43 291

LCM-MVSNet-Re		72.93 260	71.84 259	76.18 305	88.49 176	48.02 358	80.07 339	70.17 375	73.96 147	52.25 348	80.09 309	49.98 240	88.24 327	67.35 219	84.23 145	92.28 171

pm-mvs1		72.89 261	71.09 265	78.26 281	79.10 327	57.62 306	90.80 208	89.30 244	67.66 267	62.91 299	81.78 278	49.11 252	92.95 247	60.29 278	58.89 340	84.22 303

tpmvs		72.88 262	69.76 278	82.22 196	90.98 122	67.05 109	78.22 349	88.30 285	63.10 304	64.35 286	74.98 345	55.09 192	94.27 206	43.25 344	69.57 257	85.34 293

test0.0.03 1		72.76 263	72.71 249	72.88 328	80.25 311	47.99 359	91.22 194	89.45 238	71.51 217	62.51 303	87.66 210	53.83 205	85.06 350	50.16 314	67.84 274	85.58 286

UniMVSNet_ETH3D		72.74 264	70.53 271	79.36 267	78.62 335	56.64 317	85.01 296	89.20 248	63.77 295	64.84 278	84.44 250	34.05 339	91.86 286	63.94 253	70.89 252	89.57 217

mvs_tets		72.71 265	71.11 264	77.52 287	77.41 346	54.52 330	88.45 264	89.76 226	68.76 259	62.70 301	83.26 262	29.49 356	92.71 259	70.51 190	69.62 256	85.34 293

FMVSNet1		72.71 265	69.91 276	81.10 225	83.60 277	65.11 156	90.01 232	90.32 202	63.92 293	63.56 291	80.25 306	36.35 331	91.54 294	54.46 300	66.75 279	86.64 260

test_fmvs1_n		72.69 267	71.92 258	74.99 312	71.15 367	47.08 365	87.34 282	75.67 359	63.48 298	78.08 128	91.17 154	20.16 377	87.87 330	84.65 81	75.57 217	90.01 210

IterMVS		72.65 268	70.83 266	78.09 283	82.17 292	62.96 216	87.64 278	86.28 310	71.56 215	60.44 311	78.85 318	45.42 282	86.66 341	63.30 259	61.83 320	84.65 301
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 269	72.74 247	72.10 336	87.87 198	49.45 353	88.07 268	89.01 260	72.91 169	63.11 295	88.10 202	63.63 92	85.54 346	32.73 378	69.23 261	81.32 338

PatchMatch-RL		72.06 270	69.98 273	78.28 280	89.51 152	55.70 323	83.49 305	83.39 339	61.24 319	63.72 290	82.76 266	34.77 336	93.03 244	53.37 306	77.59 198	86.12 275

PVSNet_0	68.08 15	71.81 271	68.32 288	82.27 193	84.68 258	62.31 232	88.68 260	90.31 205	75.84 119	57.93 329	80.65 300	37.85 319	94.19 210	69.94 193	29.05 392	90.31 206

MIMVSNet		71.64 272	68.44 286	81.23 220	81.97 295	64.44 170	73.05 362	88.80 269	69.67 246	64.59 279	74.79 346	32.79 343	87.82 331	53.99 302	76.35 212	91.42 187

test_vis1_n		71.63 273	70.73 269	74.31 319	69.63 373	47.29 364	86.91 286	72.11 370	63.21 302	75.18 158	90.17 172	20.40 375	85.76 345	84.59 82	74.42 224	89.87 211

bld_raw_dy_0_64		71.59 274	69.71 279	77.22 295	77.82 344	58.12 299	87.71 276	73.66 366	68.01 264	61.90 307	84.29 252	33.68 340	88.43 325	69.91 194	70.43 253	85.11 296

IterMVS-SCA-FT		71.55 275	69.97 274	76.32 303	81.48 297	60.67 266	87.64 278	85.99 315	66.17 279	59.50 316	78.88 317	45.53 280	83.65 358	62.58 265	61.93 319	84.63 302

v7n		71.31 276	68.65 283	79.28 268	76.40 350	60.77 260	86.71 289	89.45 238	64.17 292	58.77 323	78.24 321	44.59 287	93.54 235	57.76 288	61.75 322	83.52 311

anonymousdsp		71.14 277	69.37 281	76.45 302	72.95 362	54.71 329	84.19 300	88.88 265	61.92 315	62.15 304	79.77 312	38.14 315	91.44 299	68.90 207	67.45 275	83.21 317

F-COLMAP		70.66 278	68.44 286	77.32 292	86.37 230	55.91 321	88.00 270	86.32 309	56.94 343	57.28 332	88.07 204	33.58 341	92.49 270	51.02 310	68.37 268	83.55 309

WR-MVS_H		70.59 279	69.94 275	72.53 330	81.03 300	51.43 342	87.35 281	92.03 134	67.38 270	60.23 313	80.70 297	55.84 184	83.45 360	46.33 334	58.58 342	82.72 324

CP-MVSNet		70.50 280	69.91 276	72.26 333	80.71 304	51.00 345	87.23 283	90.30 206	67.84 265	59.64 315	82.69 267	50.23 239	82.30 368	51.28 309	59.28 338	83.46 313

RPMNet		70.42 281	65.68 300	84.63 131	83.15 281	67.96 85	70.25 366	90.45 196	46.83 372	69.97 222	65.10 371	56.48 177	95.30 168	35.79 369	73.13 233	90.64 202

testing3		70.38 282	70.83 266	69.03 347	85.82 241	43.93 375	90.72 211	90.56 195	68.06 263	60.24 312	86.82 223	64.83 76	84.12 352	26.33 385	64.10 302	79.04 359

tfpnnormal		70.10 283	67.36 291	78.32 279	83.45 279	60.97 256	88.85 257	92.77 106	64.85 288	60.83 310	78.53 319	43.52 291	93.48 237	31.73 381	61.70 324	80.52 347

TransMVSNet (Re)		70.07 284	67.66 290	77.31 293	80.62 307	59.13 290	91.78 167	84.94 324	65.97 280	60.08 314	80.44 302	50.78 233	91.87 285	48.84 320	45.46 370	80.94 342

CL-MVSNet_self_test		69.92 285	68.09 289	75.41 308	73.25 361	55.90 322	90.05 231	89.90 222	69.96 242	61.96 306	76.54 335	51.05 232	87.64 334	49.51 318	50.59 362	82.70 326

DP-MVS		69.90 286	66.48 293	80.14 246	95.36 28	62.93 217	89.56 241	76.11 357	50.27 363	57.69 330	85.23 239	39.68 303	95.73 145	33.35 374	71.05 251	81.78 336

PS-CasMVS		69.86 287	69.13 282	72.07 337	80.35 309	50.57 347	87.02 285	89.75 227	67.27 271	59.19 319	82.28 271	46.58 270	82.24 369	50.69 311	59.02 339	83.39 315

Syy-MVS		69.65 288	69.52 280	70.03 343	87.87 198	43.21 376	88.07 268	89.01 260	72.91 169	63.11 295	88.10 202	45.28 283	85.54 346	22.07 389	69.23 261	81.32 338

MSDG		69.54 289	65.73 299	80.96 230	85.11 254	63.71 194	84.19 300	83.28 340	56.95 342	54.50 339	84.03 253	31.50 349	96.03 135	42.87 348	69.13 263	83.14 319

PEN-MVS		69.46 290	68.56 284	72.17 335	79.27 322	49.71 351	86.90 287	89.24 246	67.24 274	59.08 320	82.51 270	47.23 266	83.54 359	48.42 322	57.12 343	83.25 316

LS3D		69.17 291	66.40 295	77.50 288	91.92 100	56.12 320	85.12 295	80.37 351	46.96 370	56.50 334	87.51 213	37.25 323	93.71 232	32.52 380	79.40 182	82.68 327

PatchT		69.11 292	65.37 304	80.32 239	82.07 294	63.68 197	67.96 375	87.62 298	50.86 361	69.37 226	65.18 370	57.09 163	88.53 324	41.59 353	66.60 280	88.74 227

KD-MVS_2432*1600		69.03 293	66.37 296	77.01 297	85.56 245	61.06 254	81.44 325	90.25 208	67.27 271	58.00 327	76.53 336	54.49 197	87.63 335	48.04 324	35.77 384	82.34 330

miper_refine_blended		69.03 293	66.37 296	77.01 297	85.56 245	61.06 254	81.44 325	90.25 208	67.27 271	58.00 327	76.53 336	54.49 197	87.63 335	48.04 324	35.77 384	82.34 330

mvsany_test1		68.77 295	68.56 284	69.39 345	73.57 360	45.88 370	80.93 330	60.88 388	59.65 330	71.56 203	90.26 170	43.22 292	75.05 378	74.26 158	62.70 311	87.25 253

ACMH	63.93 17	68.62 296	64.81 306	80.03 250	85.22 250	63.25 208	87.72 275	84.66 326	60.83 322	51.57 351	79.43 316	27.29 362	94.96 176	41.76 351	64.84 294	81.88 334
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 297	65.41 303	77.96 284	78.69 333	62.93 217	89.86 237	89.17 250	60.55 323	50.27 356	77.73 326	22.60 371	94.06 217	47.18 330	72.65 239	76.88 368

ADS-MVSNet		68.54 298	64.38 313	81.03 229	88.06 192	66.90 113	68.01 373	84.02 331	57.57 337	64.48 282	69.87 362	38.68 306	89.21 320	40.87 355	67.89 272	86.97 255

DTE-MVSNet		68.46 299	67.33 292	71.87 339	77.94 342	49.00 356	86.16 292	88.58 279	66.36 278	58.19 324	82.21 273	46.36 271	83.87 357	44.97 341	55.17 350	82.73 323

our_test_3		68.29 300	64.69 308	79.11 273	78.92 328	64.85 163	88.40 265	85.06 322	60.32 326	52.68 346	76.12 340	40.81 300	89.80 317	44.25 343	55.65 348	82.67 328

Patchmatch-RL test		68.17 301	64.49 311	79.19 269	71.22 366	53.93 332	70.07 368	71.54 374	69.22 251	56.79 333	62.89 374	56.58 175	88.61 321	69.53 198	52.61 357	95.03 76

XVG-ACMP-BASELINE		68.04 302	65.53 302	75.56 307	74.06 359	52.37 337	78.43 346	85.88 316	62.03 313	58.91 322	81.21 293	20.38 376	91.15 301	60.69 275	68.18 269	83.16 318

FMVSNet5		68.04 302	65.66 301	75.18 311	84.43 265	57.89 300	83.54 304	86.26 311	61.83 317	53.64 344	73.30 349	37.15 326	85.08 349	48.99 319	61.77 321	82.56 329

ppachtmachnet_test		67.72 304	63.70 315	79.77 260	78.92 328	66.04 133	88.68 260	82.90 342	60.11 328	55.45 336	75.96 341	39.19 305	90.55 303	39.53 359	52.55 358	82.71 325

ACMH+	65.35 16	67.65 305	64.55 309	76.96 299	84.59 261	57.10 312	88.08 267	80.79 348	58.59 336	53.00 345	81.09 295	26.63 364	92.95 247	46.51 332	61.69 325	80.82 343

pmmvs6		67.57 306	64.76 307	76.00 306	72.82 364	53.37 334	88.71 259	86.78 308	53.19 354	57.58 331	78.03 324	35.33 335	92.41 272	55.56 296	54.88 352	82.21 332

Anonymous20231206		67.53 307	65.78 298	72.79 329	74.95 355	47.59 361	88.23 266	87.32 300	61.75 318	58.07 326	77.29 329	37.79 320	87.29 339	42.91 346	63.71 306	83.48 312

Patchmtry		67.53 307	63.93 314	78.34 278	82.12 293	64.38 174	68.72 370	84.00 332	48.23 369	59.24 317	72.41 352	57.82 157	89.27 319	46.10 335	56.68 347	81.36 337

USDC		67.43 309	64.51 310	76.19 304	77.94 342	55.29 325	78.38 347	85.00 323	73.17 162	48.36 363	80.37 303	21.23 373	92.48 271	52.15 308	64.02 304	80.81 344

ADS-MVSNet2		66.90 310	63.44 317	77.26 294	88.06 192	60.70 265	68.01 373	75.56 361	57.57 337	64.48 282	69.87 362	38.68 306	84.10 353	40.87 355	67.89 272	86.97 255

CMPMVS	48.56 21	66.77 311	64.41 312	73.84 321	70.65 370	50.31 348	77.79 351	85.73 318	45.54 374	44.76 373	82.14 274	35.40 334	90.14 313	63.18 260	74.54 222	81.07 341
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 312	62.92 320	76.80 301	76.51 349	57.77 302	89.22 250	83.41 338	55.48 349	53.86 343	77.84 325	26.28 365	93.95 226	34.90 371	68.76 265	78.68 362

LTVRE_ROB	59.60 19	66.27 313	63.54 316	74.45 316	84.00 272	51.55 341	67.08 376	83.53 336	58.78 334	54.94 338	80.31 304	34.54 337	93.23 241	40.64 357	68.03 270	78.58 363
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		66.06 314	62.45 323	76.88 300	81.42 299	54.45 331	57.49 388	88.67 275	49.36 365	63.86 288	46.86 386	56.06 181	90.25 307	49.53 317	68.83 264	85.95 279

Patchmatch-test		65.86 315	60.94 329	80.62 236	83.75 274	58.83 292	58.91 387	75.26 363	44.50 377	50.95 355	77.09 332	58.81 149	87.90 329	35.13 370	64.03 303	95.12 72

UnsupCasMVSNet_eth		65.79 316	63.10 318	73.88 320	70.71 369	50.29 349	81.09 328	89.88 223	72.58 176	49.25 361	74.77 347	32.57 345	87.43 338	55.96 295	41.04 377	83.90 306

test_fmvs2		65.78 317	64.84 305	68.60 349	66.54 378	41.71 378	83.27 309	69.81 376	54.38 351	67.91 249	84.54 249	15.35 382	81.22 373	75.65 145	66.16 283	82.88 320

dmvs_testset		65.55 318	66.45 294	62.86 359	79.87 315	22.35 402	76.55 354	71.74 372	77.42 103	55.85 335	87.77 209	51.39 229	80.69 374	31.51 384	65.92 285	85.55 288

pmmvs-eth3d		65.53 319	62.32 324	75.19 310	69.39 374	59.59 280	82.80 316	83.43 337	62.52 309	51.30 353	72.49 350	32.86 342	87.16 340	55.32 297	50.73 361	78.83 361

SixPastTwentyTwo		64.92 320	61.78 327	74.34 318	78.74 332	49.76 350	83.42 308	79.51 354	62.86 305	50.27 356	77.35 327	30.92 354	90.49 305	45.89 336	47.06 367	82.78 321

OurMVSNet-221017-0		64.68 321	62.17 325	72.21 334	76.08 353	47.35 362	80.67 331	81.02 347	56.19 346	51.60 350	79.66 314	27.05 363	88.56 323	53.60 305	53.63 355	80.71 345

test_0402		64.54 322	61.09 328	74.92 313	84.10 271	60.75 262	87.95 271	79.71 353	52.03 356	52.41 347	77.20 330	32.21 347	91.64 290	23.14 387	61.03 328	72.36 376

testgi		64.48 323	62.87 321	69.31 346	71.24 365	40.62 381	85.49 293	79.92 352	65.36 285	54.18 341	83.49 260	23.74 369	84.55 351	41.60 352	60.79 331	82.77 322

RPSCF		64.24 324	61.98 326	71.01 341	76.10 352	45.00 371	75.83 358	75.94 358	46.94 371	58.96 321	84.59 247	31.40 350	82.00 370	47.76 328	60.33 336	86.04 276

EU-MVSNet		64.01 325	63.01 319	67.02 355	74.40 358	38.86 386	83.27 309	86.19 313	45.11 375	54.27 340	81.15 294	36.91 329	80.01 376	48.79 321	57.02 344	82.19 333

test20.03		63.83 326	62.65 322	67.38 354	70.58 371	39.94 382	86.57 290	84.17 329	63.29 300	51.86 349	77.30 328	37.09 327	82.47 366	38.87 363	54.13 354	79.73 353

MDA-MVSNet_test_wron		63.78 327	60.16 330	74.64 314	78.15 340	60.41 269	83.49 305	84.03 330	56.17 348	39.17 382	71.59 358	37.22 324	83.24 363	42.87 348	48.73 364	80.26 350

YYNet1		63.76 328	60.14 331	74.62 315	78.06 341	60.19 274	83.46 307	83.99 334	56.18 347	39.25 381	71.56 359	37.18 325	83.34 361	42.90 347	48.70 365	80.32 349

K. test v3		63.09 329	59.61 333	73.53 323	76.26 351	49.38 355	83.27 309	77.15 356	64.35 291	47.77 365	72.32 354	28.73 358	87.79 332	49.93 316	36.69 383	83.41 314

COLMAP_ROB	57.96 20	62.98 330	59.65 332	72.98 327	81.44 298	53.00 336	83.75 303	75.53 362	48.34 368	48.81 362	81.40 287	24.14 367	90.30 306	32.95 376	60.52 333	75.65 371
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 331	59.08 334	71.10 340	67.19 377	48.72 357	83.91 302	85.23 321	50.38 362	47.84 364	71.22 361	20.74 374	85.51 348	46.47 333	58.75 341	79.06 358

AllTest		61.66 332	58.06 336	72.46 331	79.57 317	51.42 343	80.17 337	68.61 378	51.25 359	45.88 367	81.23 289	19.86 378	86.58 342	38.98 361	57.01 345	79.39 355

UnsupCasMVSNet_bld		61.60 333	57.71 337	73.29 325	68.73 375	51.64 340	78.61 345	89.05 259	57.20 341	46.11 366	61.96 377	28.70 359	88.60 322	50.08 315	38.90 381	79.63 354

MDA-MVSNet-bldmvs		61.54 334	57.70 338	73.05 326	79.53 319	57.00 316	83.08 313	81.23 346	57.57 337	34.91 385	72.45 351	32.79 343	86.26 344	35.81 368	41.95 375	75.89 370

KD-MVS_self_test		60.87 335	58.60 335	67.68 352	66.13 379	39.93 383	75.63 359	84.70 325	57.32 340	49.57 359	68.45 365	29.55 355	82.87 364	48.09 323	47.94 366	80.25 351

TinyColmap		60.32 336	56.42 343	72.00 338	78.78 331	53.18 335	78.36 348	75.64 360	52.30 355	41.59 380	75.82 343	14.76 385	88.35 326	35.84 367	54.71 353	74.46 372

MVS-HIRNet		60.25 337	55.55 344	74.35 317	84.37 266	56.57 318	71.64 364	74.11 365	34.44 385	45.54 371	42.24 392	31.11 353	89.81 315	40.36 358	76.10 214	76.67 369

MIMVSNet1		60.16 338	57.33 339	68.67 348	69.71 372	44.13 373	78.92 344	84.21 328	55.05 350	44.63 374	71.85 356	23.91 368	81.54 372	32.63 379	55.03 351	80.35 348

PM-MVS		59.40 339	56.59 341	67.84 350	63.63 381	41.86 377	76.76 353	63.22 385	59.01 333	51.07 354	72.27 355	11.72 388	83.25 362	61.34 271	50.28 363	78.39 364

new-patchmatchnet		59.30 340	56.48 342	67.79 351	65.86 380	44.19 372	82.47 317	81.77 344	59.94 329	43.65 377	66.20 369	27.67 361	81.68 371	39.34 360	41.40 376	77.50 367

test_vis1_rt		59.09 341	57.31 340	64.43 357	68.44 376	46.02 369	83.05 314	48.63 397	51.96 357	49.57 359	63.86 373	16.30 380	80.20 375	71.21 182	62.79 310	67.07 382

test_fmvs3		56.82 342	54.86 345	62.69 360	53.59 391	35.47 388	75.87 357	65.64 383	43.91 378	55.10 337	71.43 360	6.91 396	74.40 381	68.64 209	52.63 356	78.20 365

DSMNet-mixed		56.78 343	54.44 346	63.79 358	63.21 382	29.44 397	64.43 379	64.10 384	42.12 382	51.32 352	71.60 357	31.76 348	75.04 379	36.23 366	65.20 291	86.87 258

pmmvs3		55.51 344	51.50 349	67.53 353	57.90 389	50.93 346	80.37 333	73.66 366	40.63 383	44.15 376	64.75 372	16.30 380	78.97 377	44.77 342	40.98 379	72.69 374

TDRefinement		55.28 345	51.58 348	66.39 356	59.53 388	46.15 368	76.23 356	72.80 368	44.60 376	42.49 378	76.28 339	15.29 383	82.39 367	33.20 375	43.75 372	70.62 378

LF4IMVS		54.01 346	52.12 347	59.69 361	62.41 384	39.91 384	68.59 371	68.28 380	42.96 381	44.55 375	75.18 344	14.09 387	68.39 387	41.36 354	51.68 359	70.78 377

N_pmnet		50.55 347	49.11 350	54.88 367	77.17 347	4.02 410	84.36 299	2.00 408	48.59 366	45.86 369	68.82 364	32.22 346	82.80 365	31.58 382	51.38 360	77.81 366

new_pmnet		49.31 348	46.44 351	57.93 362	62.84 383	40.74 380	68.47 372	62.96 386	36.48 384	35.09 384	57.81 381	14.97 384	72.18 383	32.86 377	46.44 368	60.88 384

mvsany_test3		48.86 349	46.35 352	56.41 363	46.00 397	31.67 393	62.26 381	47.25 398	43.71 379	45.54 371	68.15 366	10.84 389	64.44 395	57.95 287	35.44 386	73.13 373

test_f		46.58 350	43.45 354	55.96 364	45.18 398	32.05 392	61.18 382	49.49 396	33.39 386	42.05 379	62.48 376	7.00 395	65.56 391	47.08 331	43.21 374	70.27 379

WB-MVS		46.23 351	44.94 353	50.11 371	62.13 385	21.23 404	76.48 355	55.49 390	45.89 373	35.78 383	61.44 379	35.54 333	72.83 382	9.96 398	21.75 393	56.27 386

FPMVS		45.64 352	43.10 356	53.23 369	51.42 394	36.46 387	64.97 378	71.91 371	29.13 389	27.53 389	61.55 378	9.83 391	65.01 393	16.00 395	55.58 349	58.22 385

SSC-MVS		44.51 353	43.35 355	47.99 375	61.01 387	18.90 406	74.12 361	54.36 391	43.42 380	34.10 386	60.02 380	34.42 338	70.39 385	9.14 400	19.57 394	54.68 387

EGC-MVSNET		42.35 354	38.09 357	55.11 366	74.57 356	46.62 367	71.63 365	55.77 389	0.04 403	0.24 404	62.70 375	14.24 386	74.91 380	17.59 392	46.06 369	43.80 389

LCM-MVSNet		40.54 355	35.79 360	54.76 368	36.92 404	30.81 394	51.41 391	69.02 377	22.07 391	24.63 391	45.37 388	4.56 400	65.81 390	33.67 373	34.50 387	67.67 380

APD_test1		40.50 356	37.31 359	50.09 372	51.88 392	35.27 389	59.45 386	52.59 393	21.64 392	26.12 390	57.80 382	4.56 400	66.56 389	22.64 388	39.09 380	48.43 388

test_vis3_rt		40.46 357	37.79 358	48.47 374	44.49 399	33.35 391	66.56 377	32.84 405	32.39 387	29.65 387	39.13 395	3.91 403	68.65 386	50.17 313	40.99 378	43.40 390

ANet_high		40.27 358	35.20 361	55.47 365	34.74 405	34.47 390	63.84 380	71.56 373	48.42 367	18.80 394	41.08 393	9.52 392	64.45 394	20.18 390	8.66 401	67.49 381

test_method		38.59 359	35.16 362	48.89 373	54.33 390	21.35 403	45.32 394	53.71 392	7.41 400	28.74 388	51.62 384	8.70 393	52.87 398	33.73 372	32.89 388	72.47 375

PMMVS2		37.93 360	33.61 363	50.92 370	46.31 396	24.76 400	60.55 385	50.05 394	28.94 390	20.93 392	47.59 385	4.41 402	65.13 392	25.14 386	18.55 396	62.87 383

Gipuma		34.91 361	31.44 364	45.30 376	70.99 368	39.64 385	19.85 398	72.56 369	20.10 394	16.16 398	21.47 399	5.08 399	71.16 384	13.07 396	43.70 373	25.08 396
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 362	29.47 365	42.67 378	41.89 401	30.81 394	52.07 389	43.45 399	15.45 395	18.52 395	44.82 389	2.12 404	58.38 396	16.05 393	30.87 390	38.83 391

APD_test2		32.77 362	29.47 365	42.67 378	41.89 401	30.81 394	52.07 389	43.45 399	15.45 395	18.52 395	44.82 389	2.12 404	58.38 396	16.05 393	30.87 390	38.83 391

PMVS	26.43 22	31.84 364	28.16 367	42.89 377	25.87 407	27.58 398	50.92 392	49.78 395	21.37 393	14.17 399	40.81 394	2.01 406	66.62 388	9.61 399	38.88 382	34.49 395
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 365	24.00 369	26.45 382	43.74 400	18.44 407	60.86 383	39.66 401	15.11 397	9.53 401	22.10 398	6.52 397	46.94 400	8.31 401	10.14 398	13.98 398

MVE	24.84 23	24.35 366	19.77 372	38.09 380	34.56 406	26.92 399	26.57 396	38.87 403	11.73 399	11.37 400	27.44 396	1.37 407	50.42 399	11.41 397	14.60 397	36.93 393
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 367	23.20 371	25.46 383	41.52 403	16.90 408	60.56 384	38.79 404	14.62 398	8.99 402	20.24 401	7.35 394	45.82 401	7.25 402	9.46 399	13.64 399

tmp_tt		22.26 368	23.75 370	17.80 384	5.23 408	12.06 409	35.26 395	39.48 402	2.82 402	18.94 393	44.20 391	22.23 372	24.64 403	36.30 365	9.31 400	16.69 397

cdsmvs_eth3d_5k		19.86 369	26.47 368	0.00 388	0.00 411	0.00 413	0.00 399	93.45 81	0.00 406	0.00 407	95.27 56	49.56 244	0.00 407	0.00 406	0.00 404	0.00 403

wuyk23d		11.30 370	10.95 373	12.33 385	48.05 395	19.89 405	25.89 397	1.92 409	3.58 401	3.12 403	1.37 403	0.64 408	15.77 404	6.23 403	7.77 402	1.35 400

ab-mvs-re		7.91 371	10.55 374	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	94.95 64	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

testmvs		7.23 372	9.62 375	0.06 387	0.04 409	0.02 412	84.98 297	0.02 410	0.03 404	0.18 405	1.21 404	0.01 410	0.02 405	0.14 404	0.01 403	0.13 402

test123		6.92 373	9.21 376	0.08 386	0.03 410	0.05 411	81.65 323	0.01 411	0.02 405	0.14 406	0.85 405	0.03 409	0.02 405	0.12 405	0.00 404	0.16 401

pcd_1.5k_mvsjas		4.46 374	5.95 377	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	53.55 209	0.00 407	0.00 406	0.00 404	0.00 403

test_blank		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

uanet_test		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

DCPMVS		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

sosnet-low-res		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

sosnet		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

uncertanet		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

Regformer		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

uanet		0.00 375	0.00 378	0.00 388	0.00 411	0.00 413	0.00 399	0.00 412	0.00 406	0.00 407	0.00 406	0.00 411	0.00 407	0.00 406	0.00 404	0.00 403

WAC-MVS						49.45 353								31.56 383

FOURS1					93.95 45	61.77 241	93.96 70	91.92 138	62.14 312	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 54	71.42 219	90.67 18	96.85 16	74.45 18

eth-test2					0.00 411

eth-test					0.00 411

ZD-MVS					96.63 9	65.50 148		93.50 79	70.74 233	85.26 59	95.19 61	64.92 75	97.29 76	87.51 55	93.01 54

RE-MVS-def			80.48 134		92.02 94	58.56 295	90.90 203	90.45 196	62.76 306	78.89 117	94.46 78	49.30 247		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 45	71.65 208	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 43	71.65 208	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 54	72.76 173	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	92.66 111	78.19 86

test_0728_THIRD									72.48 178	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 49					99.15 2	91.91 27	94.90 21	96.51 21

test0726					96.40 15	69.99 33	96.76 7	94.33 51	71.92 194	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												57.85 156				94.68 89

sam_mvs												54.91 194

ambc				69.61 344	61.38 386	41.35 379	49.07 393	85.86 317		50.18 358	66.40 368	10.16 390	88.14 328	45.73 337	44.20 371	79.32 357

MTGPA								92.23 124

test_post1							78.95 343				20.70 400	53.05 214	91.50 298	60.43 276

test_post											23.01 397	56.49 176	92.67 262

patchmatchnet-post											67.62 367	57.62 159	90.25 307

GG-mvs-BLEND				86.53 65	91.91 101	69.67 46	75.02 360	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	32.52 406

gm-plane-assit					88.42 180	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 77	71.75 205	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 83	71.87 199	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 86		84.49 65			96.75 111

TestCases				72.46 331	79.57 317	51.42 343		68.61 378	51.25 359	45.88 367	81.23 289	19.86 378	86.58 342	38.98 361	57.01 345	79.39 355

test_prior4						67.18 106	93.92 73

test_prior2							95.10 38		75.40 126	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 96					96.84 108			95.05 74

旧先验2							92.00 156		59.37 332	87.54 38			93.47 238	75.39 147

新几何2							91.41 178

新几何1				84.73 124	92.32 86	64.28 179		91.46 163	59.56 331	79.77 106	92.90 118	56.95 169	96.57 116	63.40 256	92.91 56	93.34 139

旧先验1					91.94 98	60.74 263		91.50 161			94.36 82	65.23 70			91.84 69	94.55 96

无先验							92.71 121	92.61 115	62.03 313				97.01 93	66.63 227		93.97 121

原ACMM2							92.01 153

原ACMM1				84.42 138	93.21 63	64.27 180		93.40 85	65.39 284	79.51 109	92.50 126	58.11 155	96.69 112	65.27 246	93.96 38	92.32 169

test222					89.77 145	61.60 246	89.55 242	89.42 240	56.83 344	77.28 137	92.43 130	52.76 217			91.14 83	93.09 147

testdata2													96.09 129	61.26 272

segment_acmp												65.94 63

testdata				81.34 218	89.02 166	57.72 303		89.84 224	58.65 335	85.32 58	94.09 94	57.03 164	93.28 240	69.34 200	90.56 89	93.03 150

testdata1							89.21 251		77.55 99

test12				87.09 45	94.60 36	68.86 61		92.91 102		82.67 81		65.44 68	97.55 62		93.69 46	94.84 83

plane_prior7					86.94 220	61.51 247

plane_prior6					87.23 212	62.32 231						50.66 234

plane_prior5								91.31 167					95.55 158	76.74 137	78.53 192	88.39 235

plane_prior4											89.14 186

plane_prior3						61.95 239			79.09 73	72.53 188

plane_prior2							93.13 105		78.81 80

plane_prior1					87.15 214

plane_prior						62.42 227	93.85 77		79.38 65						78.80 189

n2								0.00 412

nn								0.00 412

door-mid								66.01 382

lessismore_v0				73.72 322	72.93 363	47.83 360		61.72 387		45.86 369	73.76 348	28.63 360	89.81 315	47.75 329	31.37 389	83.53 310

LGP-MVS_train				79.56 265	84.31 267	59.37 284		89.73 230	69.49 247	64.86 276	88.42 191	38.65 308	94.30 204	72.56 169	72.76 237	85.01 297

test11								93.01 98

door								66.57 381

HQP5-MVS						63.66 198

HQP-NCC					87.54 205		94.06 63		79.80 57	74.18 167

ACMP_Plane					87.54 205		94.06 63		79.80 57	74.18 167

BP-MVS														77.63 134

HQP4-MVS										74.18 167			95.61 153			88.63 228

HQP3-MVS								91.70 153							78.90 187

HQP2-MVS												51.63 227

NP-MVS					87.41 208	63.04 213					90.30 168

MDTV_nov1_ep13_2view						59.90 277	80.13 338		67.65 268	72.79 183		54.33 202		59.83 280		92.58 162

MDTV_nov1_ep13			72.61 250		89.06 165	68.48 69	80.33 334	90.11 214	71.84 201	71.81 199	75.92 342	53.01 215	93.92 227	48.04 324	73.38 231

ACMMP++_ref															71.63 245

ACMMP++															69.72 255

Test By Simon												54.21 203

ITE_SJBPF				70.43 342	74.44 357	47.06 366		77.32 355	60.16 327	54.04 342	83.53 258	23.30 370	84.01 355	43.07 345	61.58 326	80.21 352

DeepMVS_CX				34.71 381	51.45 393	24.73 401		28.48 407	31.46 388	17.49 397	52.75 383	5.80 398	42.60 402	18.18 391	19.42 395	36.81 394

High-resolution multi-view benchmark