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 2	89.94 4	97.66 2	73.37 8	97.13 2	95.58 8	89.33 1	85.77 40	96.26 22	72.84 26	99.38 1	92.64 6	95.93 9	97.08 9

DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 143	76.68 2	97.29 1	95.35 10	82.87 15	91.58 10	97.22 3	79.93 5	99.10 9	83.12 76	97.64 2	97.94 1

DVP-MVS++		90.53 3	91.09 4	88.87 13	97.31 4	69.91 35	93.96 61	94.37 44	72.48 154	92.07 6	96.85 11	83.82 2	99.15 2	91.53 15	97.42 4	97.55 4

MSP-MVS		90.38 4	91.87 1	85.88 78	92.83 71	64.03 178	93.06 95	94.33 46	82.19 21	93.65 3	96.15 25	85.89 1	97.19 74	91.02 19	97.75 1	96.43 25
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 5	90.89 6	88.61 18	96.76 8	70.65 24	96.47 12	94.83 23	84.83 9	89.07 22	96.80 14	70.86 34	99.06 15	92.64 6	95.71 10	96.12 34

DELS-MVS		90.05 6	90.09 9	89.94 4	93.14 66	73.88 7	97.01 3	94.40 42	88.32 2	85.71 41	94.91 55	74.11 19	98.91 17	87.26 44	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
SED-MVS		89.94 7	90.36 8	88.70 15	96.45 12	69.38 45	96.89 4	94.44 38	71.65 183	92.11 4	97.21 4	76.79 9	99.11 6	92.34 8	95.36 13	97.62 2

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 115	93.00 69	58.16 279	96.72 7	94.41 40	86.50 6	90.25 18	97.83 1	75.46 14	98.67 23	92.78 5	95.49 12	97.32 6

patch_mono-2		89.71 9	90.99 5	85.85 81	96.04 24	63.70 187	95.04 38	95.19 13	86.74 5	91.53 11	95.15 49	73.86 20	97.58 53	93.38 3	92.00 65	96.28 31

CANet		89.61 10	89.99 10	88.46 19	94.39 39	69.71 41	96.53 11	93.78 57	86.89 4	89.68 19	95.78 29	65.94 56	99.10 9	92.99 4	93.91 38	96.58 17

DVP-MVS		89.41 11	89.73 12	88.45 20	96.40 15	69.99 31	96.64 8	94.52 34	71.92 170	90.55 16	96.93 9	73.77 21	99.08 11	91.91 13	94.90 20	96.29 29
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 12	89.95 11	87.64 28	95.10 30	68.23 75	95.24 31	94.49 36	82.43 19	88.90 23	96.35 20	71.89 33	98.63 24	88.76 32	96.40 6	96.06 35

NCCC		89.07 13	89.46 13	87.91 23	96.60 10	69.05 54	96.38 13	94.64 31	84.42 10	86.74 32	96.20 23	66.56 52	98.76 22	89.03 31	94.56 30	95.92 40

DPE-MVS		88.77 14	89.21 14	87.45 35	96.26 20	67.56 90	94.17 50	94.15 51	68.77 233	90.74 14	97.27 2	76.09 12	98.49 27	90.58 22	94.91 19	96.30 28
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		88.14 15	88.29 18	87.67 27	93.21 63	68.72 62	93.85 68	94.03 53	74.18 119	91.74 9	96.67 15	65.61 60	98.42 31	89.24 28	96.08 7	95.88 41
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 15	87.61 23	89.71 6	92.06 90	76.72 1	95.75 18	93.26 81	83.86 11	89.55 20	96.06 26	53.55 191	97.89 40	91.10 17	93.31 49	94.54 90

TSAR-MVS + MP.		88.11 17	88.64 15	86.54 61	91.73 102	68.04 79	90.36 201	93.55 70	82.89 14	91.29 12	92.89 104	72.27 30	96.03 121	87.99 35	94.77 24	95.54 50
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 18	88.37 17	86.70 55	93.51 56	65.32 145	95.15 34	93.84 56	78.17 69	85.93 39	94.80 58	75.80 13	98.21 32	89.38 25	88.78 98	96.59 15

DeepC-MVS_fast	79.48 2	87.95 19	88.00 19	87.79 26	95.86 27	68.32 70	95.74 19	94.11 52	83.82 12	83.49 60	96.19 24	64.53 72	98.44 29	83.42 75	94.88 23	96.61 14
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 20	87.38 27	89.55 11	91.41 113	76.43 3	95.74 19	93.12 89	83.53 13	89.55 20	95.95 27	53.45 195	97.68 45	91.07 18	92.62 56	94.54 90

EPNet		87.84 21	88.38 16	86.23 71	93.30 60	66.05 127	95.26 30	94.84 22	87.09 3	88.06 25	94.53 63	66.79 49	97.34 65	83.89 72	91.68 70	95.29 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 22	87.77 21	87.63 32	89.24 155	71.18 19	96.57 10	92.90 96	82.70 18	87.13 29	95.27 43	64.99 64	95.80 126	89.34 26	91.80 68	95.93 39

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 23	66.30 123	94.84 43	93.78 57	69.35 224	88.39 24	96.34 21	67.74 44	97.66 48	90.62 21	93.44 47	96.01 38

SD-MVS		87.49 24	87.49 25	87.50 34	93.60 53	68.82 60	93.90 65	92.63 107	76.86 87	87.90 26	95.76 30	66.17 53	97.63 50	89.06 30	91.48 74	96.05 36
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
dcpmvs_2		87.37 25	87.55 24	86.85 48	95.04 32	68.20 76	90.36 201	90.66 184	79.37 49	81.20 75	93.67 88	74.73 15	96.55 105	90.88 20	92.00 65	95.82 42

alignmvs		87.28 26	86.97 30	88.24 22	91.30 114	71.14 21	95.61 23	93.56 69	79.30 50	87.07 31	95.25 45	68.43 37	96.93 94	87.87 36	84.33 133	96.65 13

train_agg		87.21 27	87.42 26	86.60 57	94.18 41	67.28 97	94.16 51	93.51 71	71.87 175	85.52 43	95.33 38	68.19 39	97.27 72	89.09 29	94.90 20	95.25 67

MG-MVS		87.11 28	86.27 35	89.62 7	97.79 1	76.27 4	94.96 41	94.49 36	78.74 64	83.87 59	92.94 102	64.34 73	96.94 92	75.19 131	94.09 34	95.66 45

SF-MVS		87.03 29	87.09 28	86.84 49	92.70 77	67.45 95	93.64 77	93.76 60	70.78 207	86.25 34	96.44 19	66.98 47	97.79 43	88.68 33	94.56 30	95.28 63

CSCG		86.87 30	86.26 36	88.72 14	95.05 31	70.79 23	93.83 72	95.33 11	68.48 237	77.63 115	94.35 72	73.04 24	98.45 28	84.92 63	93.71 43	96.92 11

canonicalmvs		86.85 31	86.25 37	88.66 17	91.80 101	71.92 14	93.54 82	91.71 144	80.26 38	87.55 27	95.25 45	63.59 85	96.93 94	88.18 34	84.34 132	97.11 8

PHI-MVS		86.83 32	86.85 33	86.78 53	93.47 57	65.55 141	95.39 28	95.10 16	71.77 180	85.69 42	96.52 16	62.07 99	98.77 21	86.06 55	95.60 11	96.03 37

SteuartSystems-ACMMP		86.82 33	86.90 31	86.58 59	90.42 129	66.38 120	96.09 15	93.87 55	77.73 76	84.01 58	95.66 31	63.39 87	97.94 37	87.40 42	93.55 46	95.42 51
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 34	86.86 32	86.31 70	93.76 49	67.53 92	96.33 14	93.61 67	82.34 20	81.00 80	93.08 98	63.19 90	97.29 68	87.08 46	91.38 76	94.13 103

jason		86.40 35	86.17 38	87.11 42	86.16 219	70.54 26	95.71 22	92.19 122	82.00 23	84.58 52	94.34 73	61.86 101	95.53 146	87.76 37	90.89 82	95.27 64
jason: jason.
WTY-MVS		86.32 36	85.81 42	87.85 24	92.82 73	69.37 47	95.20 32	95.25 12	82.71 17	81.91 70	94.73 59	67.93 43	97.63 50	79.55 101	82.25 144	96.54 18

MSLP-MVS++		86.27 37	85.91 41	87.35 37	92.01 93	68.97 57	95.04 38	92.70 101	79.04 58	81.50 73	96.50 18	58.98 132	96.78 97	83.49 74	93.93 37	96.29 29

VNet		86.20 38	85.65 45	87.84 25	93.92 46	69.99 31	95.73 21	95.94 6	78.43 66	86.00 38	93.07 99	58.22 136	97.00 84	85.22 59	84.33 133	96.52 19

MVS_111021_HR		86.19 39	85.80 43	87.37 36	93.17 65	69.79 38	93.99 60	93.76 60	79.08 57	78.88 104	93.99 83	62.25 98	98.15 34	85.93 56	91.15 80	94.15 102

CS-MVS-test		86.14 40	87.01 29	83.52 147	92.63 80	59.36 267	95.49 25	91.92 131	80.09 39	85.46 45	95.53 35	61.82 103	95.77 129	86.77 50	93.37 48	95.41 52

ACMMP_NAP		86.05 41	85.80 43	86.80 52	91.58 106	67.53 92	91.79 148	93.49 74	74.93 110	84.61 51	95.30 40	59.42 126	97.92 38	86.13 53	94.92 18	94.94 76

ETV-MVS		86.01 42	86.11 39	85.70 87	90.21 134	67.02 106	93.43 87	91.92 131	81.21 31	84.13 57	94.07 82	60.93 110	95.63 137	89.28 27	89.81 90	94.46 96

APD-MVS		85.93 43	85.99 40	85.76 85	95.98 26	65.21 148	93.59 80	92.58 109	66.54 250	86.17 36	95.88 28	63.83 79	97.00 84	86.39 52	92.94 53	95.06 71
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 44	85.46 46	87.18 40	88.20 183	72.42 13	92.41 122	92.77 99	82.11 22	80.34 85	93.07 99	68.27 38	95.02 158	78.39 114	93.59 45	94.09 105

CS-MVS		85.80 45	86.65 34	83.27 155	92.00 94	58.92 272	95.31 29	91.86 136	79.97 40	84.82 50	95.40 36	62.26 97	95.51 147	86.11 54	92.08 64	95.37 55

CDPH-MVS		85.71 46	85.46 46	86.46 63	94.75 34	67.19 99	93.89 66	92.83 98	70.90 203	83.09 63	95.28 41	63.62 83	97.36 63	80.63 95	94.18 33	94.84 80

casdiffmvs_mvg		85.66 47	85.18 49	87.09 43	88.22 182	69.35 48	93.74 75	91.89 134	81.47 26	80.10 87	91.45 131	64.80 68	96.35 108	87.23 45	87.69 106	95.58 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS	77.85 3	85.52 48	85.24 48	86.37 67	88.80 165	66.64 114	92.15 128	93.68 65	81.07 32	76.91 125	93.64 89	62.59 95	98.44 29	85.50 57	92.84 55	94.03 109
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 49	84.87 55	86.84 49	88.25 180	69.07 53	93.04 97	91.76 141	81.27 30	80.84 82	92.07 122	64.23 74	96.06 119	84.98 62	87.43 109	95.39 53
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ZNCC-MVS		85.33 50	85.08 51	86.06 73	93.09 68	65.65 137	93.89 66	93.41 78	73.75 130	79.94 89	94.68 61	60.61 113	98.03 36	82.63 79	93.72 42	94.52 92

MP-MVS-pluss		85.24 51	85.13 50	85.56 90	91.42 111	65.59 139	91.54 158	92.51 111	74.56 113	80.62 83	95.64 32	59.15 130	97.00 84	86.94 48	93.80 39	94.07 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PAPR		85.15 52	84.47 56	87.18 40	96.02 25	68.29 71	91.85 146	93.00 93	76.59 92	79.03 100	95.00 50	61.59 104	97.61 52	78.16 115	89.00 97	95.63 46

MP-MVS		85.02 53	84.97 53	85.17 102	92.60 81	64.27 174	93.24 90	92.27 116	73.13 141	79.63 93	94.43 66	61.90 100	97.17 75	85.00 61	92.56 57	94.06 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 54	84.44 57	86.71 54	88.33 177	68.73 61	90.24 206	91.82 140	81.05 33	81.18 76	92.50 111	63.69 82	96.08 118	84.45 67	86.71 117	95.32 59

CHOSEN 1792x2688		84.98 55	83.45 67	89.57 10	89.94 138	75.14 5	92.07 134	92.32 114	81.87 24	75.68 134	88.27 178	60.18 116	98.60 25	80.46 97	90.27 89	94.96 75

EIA-MVS		84.84 56	84.88 54	84.69 116	91.30 114	62.36 214	93.85 68	92.04 126	79.45 47	79.33 97	94.28 76	62.42 96	96.35 108	80.05 98	91.25 79	95.38 54

HFP-MVS		84.73 57	84.40 58	85.72 86	93.75 51	65.01 154	93.50 84	93.19 85	72.19 164	79.22 98	94.93 53	59.04 131	97.67 46	81.55 86	92.21 60	94.49 95

MVS		84.66 58	82.86 82	90.06 2	90.93 120	74.56 6	87.91 250	95.54 9	68.55 235	72.35 175	94.71 60	59.78 122	98.90 18	81.29 92	94.69 29	96.74 12

GST-MVS		84.63 59	84.29 59	85.66 88	92.82 73	65.27 146	93.04 97	93.13 88	73.20 139	78.89 101	94.18 79	59.41 127	97.85 42	81.45 88	92.48 59	93.86 117

DROMVSNet		84.53 60	85.04 52	83.01 159	89.34 150	61.37 232	94.42 47	91.09 171	77.91 73	83.24 61	94.20 78	58.37 135	95.40 148	85.35 58	91.41 75	92.27 159

ACMMPR		84.37 61	84.06 60	85.28 98	93.56 54	64.37 170	93.50 84	93.15 87	72.19 164	78.85 106	94.86 56	56.69 156	97.45 57	81.55 86	92.20 61	94.02 110

region2R		84.36 62	84.03 61	85.36 96	93.54 55	64.31 172	93.43 87	92.95 94	72.16 167	78.86 105	94.84 57	56.97 151	97.53 55	81.38 90	92.11 63	94.24 98

LFMVS		84.34 63	82.73 84	89.18 12	94.76 33	73.25 9	94.99 40	91.89 134	71.90 172	82.16 69	93.49 93	47.98 241	97.05 79	82.55 80	84.82 128	97.25 7

test_yl		84.28 64	83.16 75	87.64 28	94.52 37	69.24 49	95.78 16	95.09 17	69.19 227	81.09 77	92.88 105	57.00 149	97.44 58	81.11 93	81.76 148	96.23 32

DCV-MVSNet		84.28 64	83.16 75	87.64 28	94.52 37	69.24 49	95.78 16	95.09 17	69.19 227	81.09 77	92.88 105	57.00 149	97.44 58	81.11 93	81.76 148	96.23 32

diffmvs		84.28 64	83.83 62	85.61 89	87.40 199	68.02 80	90.88 186	89.24 233	80.54 36	81.64 72	92.52 110	59.83 121	94.52 181	87.32 43	85.11 126	94.29 97
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 64	83.36 73	87.02 46	92.22 87	67.74 86	84.65 276	94.50 35	79.15 54	82.23 68	87.93 185	66.88 48	96.94 92	80.53 96	82.20 145	96.39 27

MAR-MVS		84.18 68	83.43 68	86.44 64	96.25 21	65.93 132	94.28 49	94.27 48	74.41 114	79.16 99	95.61 33	53.99 186	98.88 20	69.62 180	93.26 50	94.50 94
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 69	83.20 74	87.05 45	91.56 107	69.82 37	89.99 215	92.05 125	77.77 75	82.84 64	86.57 199	63.93 78	96.09 116	74.91 136	89.18 96	95.25 67

CANet_DTU		84.09 70	83.52 64	85.81 82	90.30 132	66.82 109	91.87 144	89.01 246	85.27 7	86.09 37	93.74 87	47.71 244	96.98 88	77.90 117	89.78 92	93.65 122

ET-MVSNet_ETH3D		84.01 71	83.15 77	86.58 59	90.78 125	70.89 22	94.74 44	94.62 32	81.44 29	58.19 300	93.64 89	73.64 23	92.35 256	82.66 78	78.66 172	96.50 23

PVSNet_Blended_VisFu		83.97 72	83.50 65	85.39 95	90.02 136	66.59 117	93.77 73	91.73 142	77.43 84	77.08 124	89.81 161	63.77 81	96.97 89	79.67 100	88.21 102	92.60 147

MTAPA		83.91 73	83.38 72	85.50 91	91.89 99	65.16 150	81.75 298	92.23 117	75.32 105	80.53 84	95.21 47	56.06 164	97.16 76	84.86 64	92.55 58	94.18 99

XVS		83.87 74	83.47 66	85.05 103	93.22 61	63.78 182	92.92 102	92.66 104	73.99 122	78.18 110	94.31 75	55.25 169	97.41 60	79.16 105	91.58 72	93.95 112

Effi-MVS+		83.82 75	82.76 83	86.99 47	89.56 146	69.40 44	91.35 169	86.12 291	72.59 151	83.22 62	92.81 108	59.60 124	96.01 123	81.76 85	87.80 105	95.56 49

EI-MVSNet-Vis-set		83.77 76	83.67 63	84.06 134	92.79 76	63.56 192	91.76 151	94.81 24	79.65 46	77.87 113	94.09 80	63.35 88	97.90 39	79.35 103	79.36 164	90.74 182

MVSFormer		83.75 77	82.88 81	86.37 67	89.24 155	71.18 19	89.07 234	90.69 181	65.80 255	87.13 29	94.34 73	64.99 64	92.67 242	72.83 147	91.80 68	95.27 64

CP-MVS		83.71 78	83.40 71	84.65 117	93.14 66	63.84 180	94.59 45	92.28 115	71.03 201	77.41 118	94.92 54	55.21 172	96.19 112	81.32 91	90.70 84	93.91 114

baseline2		83.68 79	83.42 70	84.48 123	87.37 200	66.00 129	90.06 210	95.93 7	79.71 45	69.08 209	90.39 149	77.92 6	96.28 110	78.91 109	81.38 152	91.16 178

thisisatest0515		83.41 80	82.49 89	86.16 72	89.46 149	68.26 73	93.54 82	94.70 28	74.31 117	75.75 132	90.92 139	72.62 28	96.52 106	69.64 178	81.50 151	93.71 120

PVSNet_BlendedMVS		83.38 81	83.43 68	83.22 156	93.76 49	67.53 92	94.06 56	93.61 67	79.13 55	81.00 80	85.14 214	63.19 90	97.29 68	87.08 46	73.91 207	84.83 277

test2506		83.29 82	82.92 80	84.37 126	88.39 175	63.18 198	92.01 137	91.35 159	77.66 78	78.49 109	91.42 132	64.58 71	95.09 157	73.19 143	89.23 94	94.85 77

iter_conf05		83.27 83	82.70 85	84.98 106	93.32 59	71.84 15	94.16 51	81.76 321	82.74 16	73.83 156	88.40 174	72.77 27	94.61 172	82.10 82	75.21 199	88.48 211

PGM-MVS		83.25 84	82.70 85	84.92 107	92.81 75	64.07 177	90.44 197	92.20 121	71.28 195	77.23 121	94.43 66	55.17 173	97.31 67	79.33 104	91.38 76	93.37 128

HPM-MVS		83.25 84	82.95 79	84.17 132	92.25 86	62.88 207	90.91 183	91.86 136	70.30 213	77.12 122	93.96 84	56.75 154	96.28 110	82.04 83	91.34 78	93.34 129
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 86	82.96 78	83.67 145	92.28 85	63.19 197	91.38 167	94.68 29	79.22 52	76.60 127	93.75 86	62.64 94	97.76 44	78.07 116	78.01 175	90.05 191

VDD-MVS		83.06 87	81.81 97	86.81 51	90.86 123	67.70 87	95.40 27	91.50 154	75.46 102	81.78 71	92.34 118	40.09 278	97.13 77	86.85 49	82.04 146	95.60 47

h-mvs33		83.01 88	82.56 88	84.35 127	89.34 150	62.02 219	92.72 107	93.76 60	81.45 27	82.73 65	92.25 120	60.11 117	97.13 77	87.69 38	62.96 283	93.91 114

PAPM_NR		82.97 89	81.84 96	86.37 67	94.10 44	66.76 112	87.66 255	92.84 97	69.96 217	74.07 153	93.57 91	63.10 92	97.50 56	70.66 171	90.58 86	94.85 77

mPP-MVS		82.96 90	82.44 90	84.52 121	92.83 71	62.92 205	92.76 105	91.85 138	71.52 191	75.61 137	94.24 77	53.48 194	96.99 87	78.97 108	90.73 83	93.64 123

SR-MVS		82.81 91	82.58 87	83.50 150	93.35 58	61.16 235	92.23 127	91.28 163	64.48 264	81.27 74	95.28 41	53.71 190	95.86 125	82.87 77	88.77 99	93.49 126

DP-MVS Recon		82.73 92	81.65 98	85.98 75	97.31 4	67.06 103	95.15 34	91.99 128	69.08 230	76.50 129	93.89 85	54.48 181	98.20 33	70.76 169	85.66 124	92.69 144

CLD-MVS		82.73 92	82.35 92	83.86 138	87.90 190	67.65 89	95.45 26	92.18 123	85.06 8	72.58 168	92.27 119	52.46 202	95.78 127	84.18 68	79.06 167	88.16 217
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 94	82.38 91	83.73 142	89.25 154	59.58 262	92.24 126	94.89 21	77.96 71	79.86 90	92.38 116	56.70 155	97.05 79	77.26 120	80.86 156	94.55 88

3Dnovator	73.91 6	82.69 95	80.82 109	88.31 21	89.57 145	71.26 18	92.60 115	94.39 43	78.84 61	67.89 229	92.48 114	48.42 236	98.52 26	68.80 190	94.40 32	95.15 69

MVSTER		82.47 96	82.05 93	83.74 140	92.68 78	69.01 55	91.90 143	93.21 82	79.83 41	72.14 176	85.71 211	74.72 16	94.72 168	75.72 127	72.49 218	87.50 222

TESTMET0.1,1		82.41 97	81.98 95	83.72 143	88.08 184	63.74 184	92.70 109	93.77 59	79.30 50	77.61 116	87.57 190	58.19 137	94.08 196	73.91 142	86.68 118	93.33 131

CostFormer		82.33 98	81.15 102	85.86 80	89.01 160	68.46 67	82.39 296	93.01 91	75.59 100	80.25 86	81.57 257	72.03 32	94.96 160	79.06 107	77.48 183	94.16 101

API-MVS		82.28 99	80.53 115	87.54 33	96.13 22	70.59 25	93.63 78	91.04 176	65.72 257	75.45 139	92.83 107	56.11 163	98.89 19	64.10 232	89.75 93	93.15 135

IB-MVS	77.80 4	82.18 100	80.46 117	87.35 37	89.14 157	70.28 29	95.59 24	95.17 15	78.85 60	70.19 197	85.82 209	70.66 35	97.67 46	72.19 158	66.52 258	94.09 105
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		82.16 101	81.12 103	85.26 99	86.42 213	68.72 62	92.59 117	90.44 190	73.12 142	84.20 54	94.36 68	38.04 290	95.73 131	84.12 69	86.81 112	91.33 172

xiu_mvs_v1_base		82.16 101	81.12 103	85.26 99	86.42 213	68.72 62	92.59 117	90.44 190	73.12 142	84.20 54	94.36 68	38.04 290	95.73 131	84.12 69	86.81 112	91.33 172

xiu_mvs_v1_base_debi		82.16 101	81.12 103	85.26 99	86.42 213	68.72 62	92.59 117	90.44 190	73.12 142	84.20 54	94.36 68	38.04 290	95.73 131	84.12 69	86.81 112	91.33 172

3Dnovator+	73.60 7	82.10 104	80.60 114	86.60 57	90.89 122	66.80 111	95.20 32	93.44 76	74.05 121	67.42 235	92.49 113	49.46 226	97.65 49	70.80 168	91.68 70	95.33 57

MVS_111021_LR		82.02 105	81.52 99	83.51 149	88.42 173	62.88 207	89.77 219	88.93 248	76.78 90	75.55 138	93.10 96	50.31 218	95.38 150	83.82 73	87.02 111	92.26 160

PMMVS		81.98 106	82.04 94	81.78 191	89.76 142	56.17 298	91.13 179	90.69 181	77.96 71	80.09 88	93.57 91	46.33 253	94.99 159	81.41 89	87.46 108	94.17 100

baseline1		81.84 107	81.03 107	84.28 130	91.60 105	66.62 115	91.08 180	91.66 148	81.87 24	74.86 143	91.67 129	69.98 36	94.92 163	71.76 161	64.75 272	91.29 177

EPP-MVSNet		81.79 108	81.52 99	82.61 168	88.77 166	60.21 254	93.02 99	93.66 66	68.52 236	72.90 163	90.39 149	72.19 31	94.96 160	74.93 135	79.29 166	92.67 145

iter_conf_final		81.74 109	80.93 108	84.18 131	92.66 79	69.10 52	92.94 101	82.80 319	79.01 59	74.85 144	88.40 174	61.83 102	94.61 172	79.36 102	76.52 192	88.83 202

APD-MVS_3200maxsize		81.64 110	81.32 101	82.59 169	92.36 83	58.74 274	91.39 165	91.01 177	63.35 273	79.72 92	94.62 62	51.82 205	96.14 114	79.71 99	87.93 104	92.89 143

ACMMP		81.49 111	80.67 112	83.93 137	91.71 103	62.90 206	92.13 129	92.22 120	71.79 179	71.68 183	93.49 93	50.32 217	96.96 90	78.47 113	84.22 137	91.93 164
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		81.43 112	80.74 110	83.52 147	86.26 217	64.45 164	92.09 132	90.65 185	75.83 99	73.95 155	89.81 161	63.97 77	92.91 232	71.27 164	82.82 141	93.20 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 113	79.99 122	85.46 92	90.39 131	68.40 68	86.88 267	90.61 186	74.41 114	70.31 196	84.67 220	63.79 80	92.32 257	73.13 144	85.70 123	95.67 44

ECVR-MVS		81.29 114	80.38 118	84.01 136	88.39 175	61.96 221	92.56 120	86.79 283	77.66 78	76.63 126	91.42 132	46.34 252	95.24 155	74.36 140	89.23 94	94.85 77

thisisatest0530		81.15 115	80.07 119	84.39 125	88.26 179	65.63 138	91.40 163	94.62 32	71.27 196	70.93 189	89.18 165	72.47 29	96.04 120	65.62 221	76.89 189	91.49 168

Fast-Effi-MVS+		81.14 116	80.01 121	84.51 122	90.24 133	65.86 133	94.12 55	89.15 239	73.81 129	75.37 140	88.26 179	57.26 144	94.53 180	66.97 206	84.92 127	93.15 135

HQP-MVS		81.14 116	80.64 113	82.64 167	87.54 195	63.66 190	94.06 56	91.70 146	79.80 42	74.18 149	90.30 151	51.63 209	95.61 139	77.63 118	78.90 168	88.63 207

hse-mvs2		81.12 118	81.11 106	81.16 204	86.52 212	57.48 289	89.40 227	91.16 166	81.45 27	82.73 65	90.49 147	60.11 117	94.58 174	87.69 38	60.41 310	91.41 171

SR-MVS-dyc-post		81.06 119	80.70 111	82.15 182	92.02 91	58.56 276	90.90 184	90.45 187	62.76 280	78.89 101	94.46 64	51.26 212	95.61 139	78.77 111	86.77 115	92.28 156

HyFIR lowres test		81.03 120	79.56 129	85.43 93	87.81 192	68.11 78	90.18 207	90.01 210	70.65 209	72.95 162	86.06 207	63.61 84	94.50 182	75.01 134	79.75 162	93.67 121

nrg030		80.93 121	79.86 124	84.13 133	83.69 257	68.83 59	93.23 91	91.20 164	75.55 101	75.06 142	88.22 182	63.04 93	94.74 167	81.88 84	66.88 255	88.82 205

Vis-MVSNet		80.92 122	79.98 123	83.74 140	88.48 170	61.80 223	93.44 86	88.26 269	73.96 125	77.73 114	91.76 126	49.94 222	94.76 165	65.84 218	90.37 88	94.65 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 123	80.02 120	83.33 153	87.87 191	60.76 243	92.62 114	86.86 282	77.86 74	75.73 133	91.39 134	46.35 251	94.70 171	72.79 149	88.68 100	94.52 92

1314		80.70 124	78.95 140	85.94 77	87.77 193	67.56 90	87.91 250	92.55 110	72.17 166	67.44 234	93.09 97	50.27 219	97.04 82	71.68 163	87.64 107	93.23 133

tpmrst		80.57 125	79.14 139	84.84 110	90.10 135	68.28 72	81.70 299	89.72 221	77.63 80	75.96 131	79.54 289	64.94 66	92.71 239	75.43 129	77.28 186	93.55 124

1112_ss		80.56 126	79.83 125	82.77 163	88.65 167	60.78 241	92.29 124	88.36 264	72.58 152	72.46 172	94.95 51	65.09 63	93.42 220	66.38 212	77.71 177	94.10 104

VDDNet		80.50 127	78.26 147	87.21 39	86.19 218	69.79 38	94.48 46	91.31 160	60.42 298	79.34 96	90.91 140	38.48 286	96.56 104	82.16 81	81.05 154	95.27 64

BH-w/o		80.49 128	79.30 136	84.05 135	90.83 124	64.36 171	93.60 79	89.42 228	74.35 116	69.09 208	90.15 156	55.23 171	95.61 139	64.61 229	86.43 121	92.17 162

TAMVS		80.37 129	79.45 132	83.13 158	85.14 234	63.37 193	91.23 174	90.76 180	74.81 112	72.65 166	88.49 171	60.63 112	92.95 227	69.41 182	81.95 147	93.08 138

HQP_MVS		80.34 130	79.75 126	82.12 184	86.94 206	62.42 212	93.13 93	91.31 160	78.81 62	72.53 169	89.14 167	50.66 215	95.55 144	76.74 121	78.53 173	88.39 214

HPM-MVS_fast		80.25 131	79.55 131	82.33 175	91.55 108	59.95 257	91.32 171	89.16 238	65.23 261	74.71 146	93.07 99	47.81 243	95.74 130	74.87 138	88.23 101	91.31 176

ab-mvs		80.18 132	78.31 146	85.80 83	88.44 172	65.49 144	83.00 293	92.67 103	71.82 178	77.36 119	85.01 215	54.50 178	96.59 101	76.35 125	75.63 197	95.32 59

IS-MVSNet		80.14 133	79.41 133	82.33 175	87.91 189	60.08 256	91.97 141	88.27 267	72.90 147	71.44 186	91.73 128	61.44 105	93.66 215	62.47 245	86.53 119	93.24 132

test-LLR		80.10 134	79.56 129	81.72 193	86.93 208	61.17 233	92.70 109	91.54 151	71.51 192	75.62 135	86.94 196	53.83 187	92.38 253	72.21 156	84.76 130	91.60 166

PVSNet	73.49 8	80.05 135	78.63 142	84.31 128	90.92 121	64.97 155	92.47 121	91.05 175	79.18 53	72.43 173	90.51 146	37.05 302	94.06 198	68.06 194	86.00 122	93.90 116

UA-Net		80.02 136	79.65 127	81.11 206	89.33 152	57.72 284	86.33 270	89.00 247	77.44 83	81.01 79	89.15 166	59.33 128	95.90 124	61.01 252	84.28 135	89.73 196

test-mter		79.96 137	79.38 135	81.72 193	86.93 208	61.17 233	92.70 109	91.54 151	73.85 127	75.62 135	86.94 196	49.84 224	92.38 253	72.21 156	84.76 130	91.60 166

QAPM		79.95 138	77.39 164	87.64 28	89.63 144	71.41 17	93.30 89	93.70 64	65.34 260	67.39 237	91.75 127	47.83 242	98.96 16	57.71 268	89.81 90	92.54 149

UGNet		79.87 139	78.68 141	83.45 152	89.96 137	61.51 229	92.13 129	90.79 179	76.83 89	78.85 106	86.33 203	38.16 288	96.17 113	67.93 197	87.17 110	92.67 145
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		79.80 140	77.95 153	85.34 97	88.28 178	68.26 73	81.56 301	91.42 157	70.11 215	77.59 117	80.50 275	67.40 45	94.26 190	67.34 202	77.35 184	93.51 125

thres200		79.66 141	78.33 145	83.66 146	92.54 82	65.82 135	93.06 95	96.31 3	74.90 111	73.30 159	88.66 169	59.67 123	95.61 139	47.84 306	78.67 171	89.56 199

CPTT-MVS		79.59 142	79.16 138	80.89 215	91.54 109	59.80 259	92.10 131	88.54 262	60.42 298	72.96 161	93.28 95	48.27 237	92.80 236	78.89 110	86.50 120	90.06 190

Test_1112_low_res		79.56 143	78.60 143	82.43 171	88.24 181	60.39 251	92.09 132	87.99 273	72.10 168	71.84 179	87.42 192	64.62 70	93.04 224	65.80 219	77.30 185	93.85 118

tttt0517		79.50 144	78.53 144	82.41 174	87.22 202	61.43 231	89.75 220	94.76 25	69.29 225	67.91 227	88.06 184	72.92 25	95.63 137	62.91 241	73.90 208	90.16 189

FIs		79.47 145	79.41 133	79.67 239	85.95 222	59.40 264	91.68 155	93.94 54	78.06 70	68.96 212	88.28 177	66.61 51	91.77 268	66.20 215	74.99 200	87.82 219

BH-RMVSNet		79.46 146	77.65 156	84.89 108	91.68 104	65.66 136	93.55 81	88.09 271	72.93 146	73.37 158	91.12 138	46.20 255	96.12 115	56.28 273	85.61 125	92.91 142

PCF-MVS	73.15 9	79.29 147	77.63 157	84.29 129	86.06 220	65.96 131	87.03 262	91.10 170	69.86 219	69.79 204	90.64 142	57.54 143	96.59 101	64.37 231	82.29 143	90.32 187
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 148	79.57 128	78.24 260	88.46 171	52.29 317	90.41 199	89.12 241	74.24 118	69.13 207	91.91 124	65.77 58	90.09 292	59.00 264	88.09 103	92.33 153

114514_t		79.17 149	77.67 155	83.68 144	95.32 29	65.53 142	92.85 104	91.60 150	63.49 271	67.92 226	90.63 144	46.65 248	95.72 135	67.01 205	83.54 138	89.79 194

FA-MVS(test-final)		79.12 150	77.23 166	84.81 112	90.54 127	63.98 179	81.35 304	91.71 144	71.09 200	74.85 144	82.94 238	52.85 198	97.05 79	67.97 195	81.73 150	93.41 127

VPA-MVSNet		79.03 151	78.00 151	82.11 187	85.95 222	64.48 163	93.22 92	94.66 30	75.05 109	74.04 154	84.95 216	52.17 204	93.52 217	74.90 137	67.04 254	88.32 216

OPM-MVS		79.00 152	78.09 149	81.73 192	83.52 260	63.83 181	91.64 157	90.30 197	76.36 95	71.97 178	89.93 160	46.30 254	95.17 156	75.10 132	77.70 178	86.19 250
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 153	78.22 148	81.25 201	85.33 230	62.73 210	89.53 224	93.21 82	72.39 159	72.14 176	90.13 157	60.99 108	94.72 168	67.73 199	72.49 218	86.29 246

AdaColmap		78.94 154	77.00 170	84.76 113	96.34 17	65.86 133	92.66 113	87.97 274	62.18 285	70.56 190	92.37 117	43.53 267	97.35 64	64.50 230	82.86 140	91.05 180

GeoE		78.90 155	77.43 160	83.29 154	88.95 161	62.02 219	92.31 123	86.23 289	70.24 214	71.34 187	89.27 164	54.43 182	94.04 201	63.31 237	80.81 158	93.81 119

miper_enhance_ethall		78.86 156	77.97 152	81.54 196	88.00 188	65.17 149	91.41 161	89.15 239	75.19 107	68.79 215	83.98 229	67.17 46	92.82 234	72.73 150	65.30 263	86.62 243

VPNet		78.82 157	77.53 159	82.70 165	84.52 244	66.44 119	93.93 63	92.23 117	80.46 37	72.60 167	88.38 176	49.18 230	93.13 223	72.47 154	63.97 280	88.55 210

EPNet_dtu		78.80 158	79.26 137	77.43 268	88.06 185	49.71 330	91.96 142	91.95 130	77.67 77	76.56 128	91.28 136	58.51 134	90.20 290	56.37 272	80.95 155	92.39 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 159	77.43 160	82.88 161	92.21 88	64.49 161	92.05 135	96.28 4	73.48 136	71.75 181	88.26 179	60.07 119	95.32 151	45.16 317	77.58 180	88.83 202

TR-MVS		78.77 160	77.37 165	82.95 160	90.49 128	60.88 239	93.67 76	90.07 206	70.08 216	74.51 147	91.37 135	45.69 258	95.70 136	60.12 258	80.32 159	92.29 155

thres400		78.68 161	77.43 160	82.43 171	92.21 88	64.49 161	92.05 135	96.28 4	73.48 136	71.75 181	88.26 179	60.07 119	95.32 151	45.16 317	77.58 180	87.48 223

BH-untuned		78.68 161	77.08 167	83.48 151	89.84 139	63.74 184	92.70 109	88.59 260	71.57 189	66.83 244	88.65 170	51.75 207	95.39 149	59.03 263	84.77 129	91.32 175

OMC-MVS		78.67 163	77.91 154	80.95 213	85.76 226	57.40 291	88.49 243	88.67 257	73.85 127	72.43 173	92.10 121	49.29 229	94.55 179	72.73 150	77.89 176	90.91 181

tpm		78.58 164	77.03 168	83.22 156	85.94 224	64.56 159	83.21 290	91.14 169	78.31 67	73.67 157	79.68 287	64.01 76	92.09 262	66.07 216	71.26 228	93.03 139

OpenMVS	70.45 11	78.54 165	75.92 183	86.41 66	85.93 225	71.68 16	92.74 106	92.51 111	66.49 251	64.56 260	91.96 123	43.88 266	98.10 35	54.61 278	90.65 85	89.44 200

EPMVS		78.49 166	75.98 182	86.02 74	91.21 116	69.68 42	80.23 313	91.20 164	75.25 106	72.48 171	78.11 297	54.65 177	93.69 214	57.66 269	83.04 139	94.69 83

AUN-MVS		78.37 167	77.43 160	81.17 203	86.60 211	57.45 290	89.46 226	91.16 166	74.11 120	74.40 148	90.49 147	55.52 168	94.57 176	74.73 139	60.43 309	91.48 169

thres100view900		78.37 167	77.01 169	82.46 170	91.89 99	63.21 196	91.19 178	96.33 1	72.28 162	70.45 193	87.89 186	60.31 114	95.32 151	45.16 317	77.58 180	88.83 202

GA-MVS		78.33 169	76.23 179	84.65 117	83.65 258	66.30 123	91.44 159	90.14 204	76.01 97	70.32 195	84.02 228	42.50 271	94.72 168	70.98 166	77.00 188	92.94 141

cascas		78.18 170	75.77 185	85.41 94	87.14 204	69.11 51	92.96 100	91.15 168	66.71 249	70.47 191	86.07 206	37.49 296	96.48 107	70.15 174	79.80 161	90.65 183

UniMVSNet_NR-MVSNet		78.15 171	77.55 158	79.98 231	84.46 246	60.26 252	92.25 125	93.20 84	77.50 82	68.88 213	86.61 198	66.10 54	92.13 260	66.38 212	62.55 287	87.54 221

thres600view7		78.00 172	76.66 174	82.03 189	91.93 96	63.69 188	91.30 172	96.33 1	72.43 157	70.46 192	87.89 186	60.31 114	94.92 163	42.64 329	76.64 190	87.48 223

FC-MVSNet-test		77.99 173	78.08 150	77.70 263	84.89 239	55.51 303	90.27 204	93.75 63	76.87 86	66.80 245	87.59 189	65.71 59	90.23 289	62.89 242	73.94 206	87.37 226

Anonymous202405211		77.96 174	75.33 190	85.87 79	93.73 52	64.52 160	94.85 42	85.36 297	62.52 283	76.11 130	90.18 154	29.43 329	97.29 68	68.51 192	77.24 187	95.81 43

cl22		77.94 175	76.78 172	81.42 198	87.57 194	64.93 157	90.67 192	88.86 251	72.45 156	67.63 233	82.68 242	64.07 75	92.91 232	71.79 159	65.30 263	86.44 244

XXY-MVS		77.94 175	76.44 176	82.43 171	82.60 268	64.44 165	92.01 137	91.83 139	73.59 135	70.00 200	85.82 209	54.43 182	94.76 165	69.63 179	68.02 248	88.10 218

MS-PatchMatch		77.90 177	76.50 175	82.12 184	85.99 221	69.95 34	91.75 153	92.70 101	73.97 124	62.58 279	84.44 224	41.11 275	95.78 127	63.76 235	92.17 62	80.62 322

FMVSNet3		77.73 178	76.04 181	82.80 162	91.20 117	68.99 56	91.87 144	91.99 128	73.35 138	67.04 240	83.19 237	56.62 157	92.14 259	59.80 260	69.34 237	87.28 230

miper_ehance_all_eth		77.60 179	76.44 176	81.09 210	85.70 227	64.41 168	90.65 193	88.64 259	72.31 160	67.37 238	82.52 243	64.77 69	92.64 246	70.67 170	65.30 263	86.24 248

UniMVSNet (Re)		77.58 180	76.78 172	79.98 231	84.11 252	60.80 240	91.76 151	93.17 86	76.56 93	69.93 203	84.78 219	63.32 89	92.36 255	64.89 228	62.51 289	86.78 238

PatchmatchNet		77.46 181	74.63 196	85.96 76	89.55 147	70.35 28	79.97 317	89.55 224	72.23 163	70.94 188	76.91 308	57.03 147	92.79 237	54.27 280	81.17 153	94.74 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 182	75.65 188	82.73 164	80.38 286	67.13 102	91.85 146	90.23 201	75.09 108	69.37 205	83.39 235	53.79 189	94.44 183	71.77 160	65.00 269	86.63 242

CHOSEN 280x420		77.35 183	76.95 171	78.55 255	87.07 205	62.68 211	69.71 342	82.95 317	68.80 232	71.48 185	87.27 195	66.03 55	84.00 332	76.47 124	82.81 142	88.95 201

PS-MVSNAJss		77.26 184	76.31 178	80.13 227	80.64 284	59.16 269	90.63 196	91.06 174	72.80 148	68.58 219	84.57 222	53.55 191	93.96 206	72.97 145	71.96 222	87.27 231

gg-mvs-nofinetune		77.18 185	74.31 203	85.80 83	91.42 111	68.36 69	71.78 336	94.72 27	49.61 339	77.12 122	45.92 360	77.41 8	93.98 205	67.62 200	93.16 51	95.05 72

MVP-Stereo		77.12 186	76.23 179	79.79 237	81.72 275	66.34 122	89.29 228	90.88 178	70.56 211	62.01 282	82.88 239	49.34 227	94.13 193	65.55 223	93.80 39	78.88 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
X-MVStestdata		76.86 187	74.13 207	85.05 103	93.22 61	63.78 182	92.92 102	92.66 104	73.99 122	78.18 110	10.19 375	55.25 169	97.41 60	79.16 105	91.58 72	93.95 112

DU-MVS		76.86 187	75.84 184	79.91 233	82.96 266	60.26 252	91.26 173	91.54 151	76.46 94	68.88 213	86.35 201	56.16 161	92.13 260	66.38 212	62.55 287	87.35 228

mvsmamba		76.85 189	75.71 187	80.25 224	83.07 265	59.16 269	91.44 159	80.64 326	76.84 88	67.95 225	86.33 203	46.17 256	94.24 191	76.06 126	72.92 214	87.36 227

Anonymous20240529		76.84 190	74.15 206	84.88 109	91.02 118	64.95 156	93.84 71	91.09 171	53.57 328	73.00 160	87.42 192	35.91 306	97.32 66	69.14 186	72.41 220	92.36 152

c3_l		76.83 191	75.47 189	80.93 214	85.02 237	64.18 176	90.39 200	88.11 270	71.66 182	66.65 246	81.64 255	63.58 86	92.56 247	69.31 184	62.86 284	86.04 255

WR-MVS		76.76 192	75.74 186	79.82 236	84.60 242	62.27 217	92.60 115	92.51 111	76.06 96	67.87 230	85.34 212	56.76 153	90.24 288	62.20 246	63.69 282	86.94 236

v1144		76.73 193	74.88 193	82.27 177	80.23 291	66.60 116	91.68 155	90.21 203	73.69 132	69.06 210	81.89 250	52.73 200	94.40 184	69.21 185	65.23 266	85.80 261

IterMVS-LS		76.49 194	75.18 192	80.43 219	84.49 245	62.74 209	90.64 194	88.80 252	72.40 158	65.16 254	81.72 253	60.98 109	92.27 258	67.74 198	64.65 274	86.29 246
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 195	74.55 199	82.19 181	79.14 303	67.82 84	90.26 205	89.42 228	73.75 130	68.63 218	81.89 250	51.31 211	94.09 195	71.69 162	64.84 270	84.66 278

v148		76.19 196	74.47 201	81.36 199	80.05 292	64.44 165	91.75 153	90.23 201	73.68 133	67.13 239	80.84 270	55.92 166	93.86 212	68.95 188	61.73 298	85.76 264

Effi-MVS+-dtu		76.14 197	75.28 191	78.72 254	83.22 262	55.17 305	89.87 216	87.78 275	75.42 103	67.98 224	81.43 259	45.08 262	92.52 249	75.08 133	71.63 223	88.48 211

cl____		76.07 198	74.67 194	80.28 222	85.15 233	61.76 225	90.12 208	88.73 255	71.16 197	65.43 251	81.57 257	61.15 106	92.95 227	66.54 209	62.17 291	86.13 253

DIV-MVS_self_test		76.07 198	74.67 194	80.28 222	85.14 234	61.75 226	90.12 208	88.73 255	71.16 197	65.42 252	81.60 256	61.15 106	92.94 231	66.54 209	62.16 293	86.14 251

FMVSNet2		76.07 198	74.01 209	82.26 179	88.85 162	67.66 88	91.33 170	91.61 149	70.84 204	65.98 248	82.25 246	48.03 238	92.00 264	58.46 265	68.73 243	87.10 233

v144192		76.05 201	74.03 208	82.12 184	79.50 297	66.55 118	91.39 165	89.71 222	72.30 161	68.17 222	81.33 262	51.75 207	94.03 203	67.94 196	64.19 276	85.77 262

NR-MVSNet		76.05 201	74.59 197	80.44 218	82.96 266	62.18 218	90.83 188	91.73 142	77.12 85	60.96 286	86.35 201	59.28 129	91.80 267	60.74 253	61.34 302	87.35 228

v1192		75.98 203	73.92 210	82.15 182	79.73 293	66.24 125	91.22 175	89.75 216	72.67 150	68.49 220	81.42 260	49.86 223	94.27 188	67.08 204	65.02 268	85.95 258

FE-MVS		75.97 204	73.02 220	84.82 111	89.78 140	65.56 140	77.44 328	91.07 173	64.55 263	72.66 165	79.85 285	46.05 257	96.69 99	54.97 277	80.82 157	92.21 161

eth_miper_zixun_eth		75.96 205	74.40 202	80.66 216	84.66 241	63.02 200	89.28 229	88.27 267	71.88 174	65.73 249	81.65 254	59.45 125	92.81 235	68.13 193	60.53 307	86.14 251

TranMVSNet+NR-MVSNet		75.86 206	74.52 200	79.89 234	82.44 269	60.64 248	91.37 168	91.37 158	76.63 91	67.65 232	86.21 205	52.37 203	91.55 272	61.84 248	60.81 305	87.48 223

SCA		75.82 207	72.76 224	85.01 105	86.63 210	70.08 30	81.06 306	89.19 236	71.60 188	70.01 199	77.09 306	45.53 259	90.25 285	60.43 255	73.27 210	94.68 84

LPG-MVS_test		75.82 207	74.58 198	79.56 243	84.31 249	59.37 265	90.44 197	89.73 219	69.49 222	64.86 255	88.42 172	38.65 284	94.30 186	72.56 152	72.76 215	85.01 275

GBi-Net		75.65 209	73.83 211	81.10 207	88.85 162	65.11 151	90.01 212	90.32 193	70.84 204	67.04 240	80.25 280	48.03 238	91.54 273	59.80 260	69.34 237	86.64 239

test1		75.65 209	73.83 211	81.10 207	88.85 162	65.11 151	90.01 212	90.32 193	70.84 204	67.04 240	80.25 280	48.03 238	91.54 273	59.80 260	69.34 237	86.64 239

v1921920		75.63 211	73.49 216	82.06 188	79.38 298	66.35 121	91.07 182	89.48 225	71.98 169	67.99 223	81.22 265	49.16 232	93.90 209	66.56 208	64.56 275	85.92 260

ACMP	71.68 10	75.58 212	74.23 205	79.62 241	84.97 238	59.64 260	90.80 189	89.07 244	70.39 212	62.95 275	87.30 194	38.28 287	93.87 210	72.89 146	71.45 226	85.36 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 213	73.26 218	81.61 195	80.67 283	66.82 109	89.54 223	89.27 232	71.65 183	63.30 273	80.30 279	54.99 175	94.06 198	67.33 203	62.33 290	83.94 283

tpm cat1		75.30 214	72.21 232	84.58 120	88.52 168	67.77 85	78.16 326	88.02 272	61.88 290	68.45 221	76.37 312	60.65 111	94.03 203	53.77 283	74.11 204	91.93 164

PLC	68.80 14	75.23 215	73.68 214	79.86 235	92.93 70	58.68 275	90.64 194	88.30 265	60.90 295	64.43 264	90.53 145	42.38 272	94.57 176	56.52 271	76.54 191	86.33 245
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 216	72.98 221	81.88 190	79.20 300	66.00 129	90.75 191	89.11 242	71.63 187	67.41 236	81.22 265	47.36 245	93.87 210	65.46 224	64.72 273	85.77 262

Fast-Effi-MVS+-dtu		75.04 217	73.37 217	80.07 228	80.86 280	59.52 263	91.20 177	85.38 296	71.90 172	65.20 253	84.84 218	41.46 274	92.97 226	66.50 211	72.96 213	87.73 220

dp		75.01 218	72.09 233	83.76 139	89.28 153	66.22 126	79.96 318	89.75 216	71.16 197	67.80 231	77.19 305	51.81 206	92.54 248	50.39 291	71.44 227	92.51 150

TAPA-MVS	70.22 12	74.94 219	73.53 215	79.17 248	90.40 130	52.07 318	89.19 232	89.61 223	62.69 282	70.07 198	92.67 109	48.89 235	94.32 185	38.26 343	79.97 160	91.12 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v10		74.77 220	72.54 229	81.46 197	80.33 289	66.71 113	89.15 233	89.08 243	70.94 202	63.08 274	79.86 284	52.52 201	94.04 201	65.70 220	62.17 291	83.64 285

XVG-OURS-SEG-HR		74.70 221	73.08 219	79.57 242	78.25 315	57.33 292	80.49 309	87.32 278	63.22 275	68.76 216	90.12 159	44.89 263	91.59 271	70.55 172	74.09 205	89.79 194

RRT_MVS		74.44 222	72.97 222	78.84 253	82.36 270	57.66 286	89.83 218	88.79 254	70.61 210	64.58 259	84.89 217	39.24 280	92.65 245	70.11 175	66.34 259	86.21 249

ACMM	69.62 13	74.34 223	72.73 225	79.17 248	84.25 251	57.87 282	90.36 201	89.93 211	63.17 277	65.64 250	86.04 208	37.79 294	94.10 194	65.89 217	71.52 225	85.55 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 224	72.30 231	80.32 220	91.49 110	61.66 227	90.85 187	80.72 325	56.67 320	63.85 268	90.64 142	46.75 247	90.84 280	53.79 282	75.99 196	88.47 213

XVG-OURS		74.25 225	72.46 230	79.63 240	78.45 313	57.59 288	80.33 311	87.39 277	63.86 268	68.76 216	89.62 163	40.50 277	91.72 269	69.00 187	74.25 203	89.58 197

test_fmvs1		74.07 226	73.69 213	75.22 287	78.91 307	47.34 340	89.06 236	74.69 340	63.68 270	79.41 95	91.59 130	24.36 338	87.77 312	85.22 59	76.26 194	90.55 186

CVMVSNet		74.04 227	74.27 204	73.33 303	85.33 230	43.94 351	89.53 224	88.39 263	54.33 327	70.37 194	90.13 157	49.17 231	84.05 330	61.83 249	79.36 164	91.99 163

Baseline_NR-MVSNet		73.99 228	72.83 223	77.48 267	80.78 281	59.29 268	91.79 148	84.55 304	68.85 231	68.99 211	80.70 271	56.16 161	92.04 263	62.67 243	60.98 304	81.11 316

pmmvs4		73.92 229	71.81 237	80.25 224	79.17 301	65.24 147	87.43 258	87.26 280	67.64 243	63.46 271	83.91 230	48.96 234	91.53 276	62.94 240	65.49 262	83.96 282

D2MVS		73.80 230	72.02 234	79.15 250	79.15 302	62.97 201	88.58 242	90.07 206	72.94 145	59.22 294	78.30 294	42.31 273	92.70 241	65.59 222	72.00 221	81.79 313

CR-MVSNet		73.79 231	70.82 244	82.70 165	83.15 263	67.96 81	70.25 339	84.00 309	73.67 134	69.97 201	72.41 327	57.82 140	89.48 296	52.99 286	73.13 211	90.64 184

test_djsdf		73.76 232	72.56 228	77.39 269	77.00 324	53.93 310	89.07 234	90.69 181	65.80 255	63.92 266	82.03 249	43.14 270	92.67 242	72.83 147	68.53 244	85.57 266

pmmvs5		73.35 233	71.52 239	78.86 252	78.64 311	60.61 249	91.08 180	86.90 281	67.69 240	63.32 272	83.64 231	44.33 265	90.53 282	62.04 247	66.02 261	85.46 268

Anonymous20231211		73.08 234	70.39 248	81.13 205	90.62 126	63.33 194	91.40 163	90.06 208	51.84 333	64.46 263	80.67 273	36.49 304	94.07 197	63.83 234	64.17 277	85.98 257

tt0805		73.07 235	70.73 245	80.07 228	78.37 314	57.05 294	87.78 252	92.18 123	61.23 294	67.04 240	86.49 200	31.35 323	94.58 174	65.06 227	67.12 253	88.57 209

miper_lstm_enhance		73.05 236	71.73 238	77.03 274	83.80 255	58.32 278	81.76 297	88.88 249	69.80 220	61.01 285	78.23 296	57.19 145	87.51 316	65.34 225	59.53 312	85.27 273

jajsoiax		73.05 236	71.51 240	77.67 264	77.46 321	54.83 306	88.81 238	90.04 209	69.13 229	62.85 277	83.51 233	31.16 324	92.75 238	70.83 167	69.80 233	85.43 269

LCM-MVSNet-Re		72.93 238	71.84 236	76.18 283	88.49 169	48.02 335	80.07 316	70.17 350	73.96 125	52.25 323	80.09 283	49.98 221	88.24 305	67.35 201	84.23 136	92.28 156

pm-mvs1		72.89 239	71.09 242	78.26 259	79.10 304	57.62 287	90.80 189	89.30 231	67.66 241	62.91 276	81.78 252	49.11 233	92.95 227	60.29 257	58.89 315	84.22 281

tpmvs		72.88 240	69.76 254	82.22 180	90.98 119	67.05 104	78.22 325	88.30 265	63.10 278	64.35 265	74.98 319	55.09 174	94.27 188	43.25 323	69.57 236	85.34 271

test0.0.03 1		72.76 241	72.71 226	72.88 307	80.25 290	47.99 336	91.22 175	89.45 226	71.51 192	62.51 280	87.66 188	53.83 187	85.06 327	50.16 293	67.84 251	85.58 265

UniMVSNet_ETH3D		72.74 242	70.53 247	79.36 245	78.62 312	56.64 296	85.01 274	89.20 235	63.77 269	64.84 257	84.44 224	34.05 311	91.86 266	63.94 233	70.89 230	89.57 198

mvs_tets		72.71 243	71.11 241	77.52 265	77.41 322	54.52 308	88.45 244	89.76 215	68.76 234	62.70 278	83.26 236	29.49 328	92.71 239	70.51 173	69.62 235	85.34 271

FMVSNet1		72.71 243	69.91 252	81.10 207	83.60 259	65.11 151	90.01 212	90.32 193	63.92 267	63.56 270	80.25 280	36.35 305	91.54 273	54.46 279	66.75 256	86.64 239

test_fmvs1_n		72.69 245	71.92 235	74.99 290	71.15 342	47.08 342	87.34 260	75.67 335	63.48 272	78.08 112	91.17 137	20.16 350	87.87 309	84.65 65	75.57 198	90.01 192

IterMVS		72.65 246	70.83 243	78.09 261	82.17 271	62.96 202	87.64 256	86.28 287	71.56 190	60.44 288	78.85 292	45.42 261	86.66 320	63.30 238	61.83 295	84.65 279
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		72.06 247	69.98 249	78.28 258	89.51 148	55.70 302	83.49 283	83.39 315	61.24 293	63.72 269	82.76 240	34.77 309	93.03 225	53.37 285	77.59 179	86.12 254

PVSNet_0	68.08 15	71.81 248	68.32 263	82.27 177	84.68 240	62.31 216	88.68 240	90.31 196	75.84 98	57.93 305	80.65 274	37.85 293	94.19 192	69.94 176	29.05 367	90.31 188

MIMVSNet		71.64 249	68.44 261	81.23 202	81.97 274	64.44 165	73.05 335	88.80 252	69.67 221	64.59 258	74.79 320	32.79 315	87.82 310	53.99 281	76.35 193	91.42 170

test_vis1_n		71.63 250	70.73 245	74.31 297	69.63 348	47.29 341	86.91 265	72.11 346	63.21 276	75.18 141	90.17 155	20.40 348	85.76 324	84.59 66	74.42 202	89.87 193

bld_raw_dy_0_64		71.59 251	69.71 255	77.22 273	77.82 320	58.12 280	87.71 254	73.66 342	68.01 238	61.90 284	84.29 226	33.68 312	88.43 303	69.91 177	70.43 231	85.11 274

IterMVS-SCA-FT		71.55 252	69.97 250	76.32 281	81.48 276	60.67 247	87.64 256	85.99 292	66.17 253	59.50 292	78.88 291	45.53 259	83.65 334	62.58 244	61.93 294	84.63 280

v7n		71.31 253	68.65 258	79.28 246	76.40 326	60.77 242	86.71 268	89.45 226	64.17 266	58.77 299	78.24 295	44.59 264	93.54 216	57.76 267	61.75 297	83.52 288

anonymousdsp		71.14 254	69.37 256	76.45 280	72.95 337	54.71 307	84.19 278	88.88 249	61.92 289	62.15 281	79.77 286	38.14 289	91.44 278	68.90 189	67.45 252	83.21 294

F-COLMAP		70.66 255	68.44 261	77.32 270	86.37 216	55.91 300	88.00 248	86.32 286	56.94 318	57.28 308	88.07 183	33.58 313	92.49 250	51.02 289	68.37 245	83.55 286

WR-MVS_H		70.59 256	69.94 251	72.53 309	81.03 279	51.43 321	87.35 259	92.03 127	67.38 244	60.23 289	80.70 271	55.84 167	83.45 336	46.33 313	58.58 317	82.72 301

CP-MVSNet		70.50 257	69.91 252	72.26 312	80.71 282	51.00 324	87.23 261	90.30 197	67.84 239	59.64 291	82.69 241	50.23 220	82.30 344	51.28 288	59.28 313	83.46 290

RPMNet		70.42 258	65.68 275	84.63 119	83.15 263	67.96 81	70.25 339	90.45 187	46.83 347	69.97 201	65.10 346	56.48 160	95.30 154	35.79 348	73.13 211	90.64 184

tfpnnormal		70.10 259	67.36 266	78.32 257	83.45 261	60.97 238	88.85 237	92.77 99	64.85 262	60.83 287	78.53 293	43.52 268	93.48 218	31.73 359	61.70 299	80.52 323

TransMVSNet (Re)		70.07 260	67.66 265	77.31 271	80.62 285	59.13 271	91.78 150	84.94 301	65.97 254	60.08 290	80.44 276	50.78 214	91.87 265	48.84 299	45.46 345	80.94 318

CL-MVSNet_self_test		69.92 261	68.09 264	75.41 286	73.25 336	55.90 301	90.05 211	89.90 212	69.96 217	61.96 283	76.54 309	51.05 213	87.64 313	49.51 297	50.59 337	82.70 303

DP-MVS		69.90 262	66.48 269	80.14 226	95.36 28	62.93 203	89.56 221	76.11 333	50.27 338	57.69 306	85.23 213	39.68 279	95.73 131	33.35 353	71.05 229	81.78 314

PS-CasMVS		69.86 263	69.13 257	72.07 315	80.35 288	50.57 326	87.02 263	89.75 216	67.27 245	59.19 295	82.28 245	46.58 249	82.24 345	50.69 290	59.02 314	83.39 292

MSDG		69.54 264	65.73 274	80.96 212	85.11 236	63.71 186	84.19 278	83.28 316	56.95 317	54.50 314	84.03 227	31.50 321	96.03 121	42.87 327	69.13 240	83.14 296

PEN-MVS		69.46 265	68.56 259	72.17 314	79.27 299	49.71 330	86.90 266	89.24 233	67.24 248	59.08 296	82.51 244	47.23 246	83.54 335	48.42 301	57.12 318	83.25 293

LS3D		69.17 266	66.40 270	77.50 266	91.92 97	56.12 299	85.12 273	80.37 327	46.96 345	56.50 310	87.51 191	37.25 297	93.71 213	32.52 358	79.40 163	82.68 304

PatchT		69.11 267	65.37 279	80.32 220	82.07 273	63.68 189	67.96 348	87.62 276	50.86 336	69.37 205	65.18 345	57.09 146	88.53 302	41.59 332	66.60 257	88.74 206

KD-MVS_2432*1600		69.03 268	66.37 271	77.01 275	85.56 228	61.06 236	81.44 302	90.25 199	67.27 245	58.00 303	76.53 310	54.49 179	87.63 314	48.04 303	35.77 359	82.34 307

miper_refine_blended		69.03 268	66.37 271	77.01 275	85.56 228	61.06 236	81.44 302	90.25 199	67.27 245	58.00 303	76.53 310	54.49 179	87.63 314	48.04 303	35.77 359	82.34 307

MVS_0304		68.99 270	67.23 268	74.28 298	80.36 287	52.54 315	87.01 264	86.36 285	59.89 304	66.22 247	73.56 323	24.25 339	88.03 307	57.34 270	70.11 232	82.27 309

mvsany_test1		68.77 271	68.56 259	69.39 322	73.57 335	45.88 347	80.93 307	60.88 363	59.65 305	71.56 184	90.26 153	43.22 269	75.05 353	74.26 141	62.70 286	87.25 232

ACMH	63.93 17	68.62 272	64.81 281	80.03 230	85.22 232	63.25 195	87.72 253	84.66 303	60.83 296	51.57 326	79.43 290	27.29 334	94.96 160	41.76 330	64.84 270	81.88 312
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 273	65.41 278	77.96 262	78.69 310	62.93 203	89.86 217	89.17 237	60.55 297	50.27 331	77.73 300	22.60 344	94.06 198	47.18 309	72.65 217	76.88 343

ADS-MVSNet		68.54 274	64.38 288	81.03 211	88.06 185	66.90 108	68.01 346	84.02 308	57.57 312	64.48 261	69.87 337	38.68 282	89.21 298	40.87 334	67.89 249	86.97 234

DTE-MVSNet		68.46 275	67.33 267	71.87 317	77.94 318	49.00 333	86.16 271	88.58 261	66.36 252	58.19 300	82.21 247	46.36 250	83.87 333	44.97 320	55.17 325	82.73 300

our_test_3		68.29 276	64.69 283	79.11 251	78.92 305	64.85 158	88.40 245	85.06 299	60.32 300	52.68 321	76.12 314	40.81 276	89.80 295	44.25 322	55.65 323	82.67 305

Patchmatch-RL test		68.17 277	64.49 286	79.19 247	71.22 341	53.93 310	70.07 341	71.54 349	69.22 226	56.79 309	62.89 349	56.58 158	88.61 299	69.53 181	52.61 332	95.03 74

XVG-ACMP-BASELINE		68.04 278	65.53 277	75.56 285	74.06 334	52.37 316	78.43 322	85.88 293	62.03 287	58.91 298	81.21 267	20.38 349	91.15 279	60.69 254	68.18 246	83.16 295

FMVSNet5		68.04 278	65.66 276	75.18 289	84.43 247	57.89 281	83.54 282	86.26 288	61.83 291	53.64 319	73.30 324	37.15 300	85.08 326	48.99 298	61.77 296	82.56 306

ppachtmachnet_test		67.72 280	63.70 290	79.77 238	78.92 305	66.04 128	88.68 240	82.90 318	60.11 302	55.45 311	75.96 315	39.19 281	90.55 281	39.53 338	52.55 333	82.71 302

ACMH+	65.35 16	67.65 281	64.55 284	76.96 277	84.59 243	57.10 293	88.08 247	80.79 324	58.59 311	53.00 320	81.09 269	26.63 336	92.95 227	46.51 311	61.69 300	80.82 319

pmmvs6		67.57 282	64.76 282	76.00 284	72.82 339	53.37 312	88.71 239	86.78 284	53.19 329	57.58 307	78.03 298	35.33 308	92.41 252	55.56 275	54.88 327	82.21 310

Anonymous20231206		67.53 283	65.78 273	72.79 308	74.95 330	47.59 338	88.23 246	87.32 278	61.75 292	58.07 302	77.29 303	37.79 294	87.29 318	42.91 325	63.71 281	83.48 289

Patchmtry		67.53 283	63.93 289	78.34 256	82.12 272	64.38 169	68.72 343	84.00 309	48.23 344	59.24 293	72.41 327	57.82 140	89.27 297	46.10 314	56.68 322	81.36 315

USDC		67.43 285	64.51 285	76.19 282	77.94 318	55.29 304	78.38 323	85.00 300	73.17 140	48.36 338	80.37 277	21.23 346	92.48 251	52.15 287	64.02 279	80.81 320

ADS-MVSNet2		66.90 286	63.44 292	77.26 272	88.06 185	60.70 246	68.01 346	75.56 337	57.57 312	64.48 261	69.87 337	38.68 282	84.10 329	40.87 334	67.89 249	86.97 234

CMPMVS	48.56 21	66.77 287	64.41 287	73.84 300	70.65 345	50.31 327	77.79 327	85.73 295	45.54 348	44.76 348	82.14 248	35.40 307	90.14 291	63.18 239	74.54 201	81.07 317
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 288	62.92 295	76.80 279	76.51 325	57.77 283	89.22 230	83.41 314	55.48 324	53.86 318	77.84 299	26.28 337	93.95 207	34.90 350	68.76 242	78.68 337

LTVRE_ROB	59.60 19	66.27 289	63.54 291	74.45 294	84.00 254	51.55 320	67.08 349	83.53 312	58.78 309	54.94 313	80.31 278	34.54 310	93.23 222	40.64 336	68.03 247	78.58 338
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		66.06 290	62.45 298	76.88 278	81.42 278	54.45 309	57.49 361	88.67 257	49.36 340	63.86 267	46.86 359	56.06 164	90.25 285	49.53 296	68.83 241	85.95 258

Patchmatch-test		65.86 291	60.94 304	80.62 217	83.75 256	58.83 273	58.91 360	75.26 339	44.50 351	50.95 330	77.09 306	58.81 133	87.90 308	35.13 349	64.03 278	95.12 70

UnsupCasMVSNet_eth		65.79 292	63.10 293	73.88 299	70.71 344	50.29 328	81.09 305	89.88 213	72.58 152	49.25 336	74.77 321	32.57 317	87.43 317	55.96 274	41.04 352	83.90 284

test_fmvs2		65.78 293	64.84 280	68.60 325	66.54 353	41.71 353	83.27 287	69.81 351	54.38 326	67.91 227	84.54 223	15.35 355	81.22 349	75.65 128	66.16 260	82.88 297

pmmvs-eth3d		65.53 294	62.32 299	75.19 288	69.39 349	59.59 261	82.80 294	83.43 313	62.52 283	51.30 328	72.49 325	32.86 314	87.16 319	55.32 276	50.73 336	78.83 336

SixPastTwentyTwo		64.92 295	61.78 302	74.34 296	78.74 309	49.76 329	83.42 286	79.51 330	62.86 279	50.27 331	77.35 301	30.92 326	90.49 283	45.89 315	47.06 342	82.78 298

OurMVSNet-221017-0		64.68 296	62.17 300	72.21 313	76.08 329	47.35 339	80.67 308	81.02 323	56.19 321	51.60 325	79.66 288	27.05 335	88.56 301	53.60 284	53.63 330	80.71 321

test_0402		64.54 297	61.09 303	74.92 291	84.10 253	60.75 244	87.95 249	79.71 329	52.03 331	52.41 322	77.20 304	32.21 319	91.64 270	23.14 362	61.03 303	72.36 351

testgi		64.48 298	62.87 296	69.31 323	71.24 340	40.62 356	85.49 272	79.92 328	65.36 259	54.18 316	83.49 234	23.74 342	84.55 328	41.60 331	60.79 306	82.77 299

RPSCF		64.24 299	61.98 301	71.01 319	76.10 328	45.00 348	75.83 332	75.94 334	46.94 346	58.96 297	84.59 221	31.40 322	82.00 346	47.76 307	60.33 311	86.04 255

EU-MVSNet		64.01 300	63.01 294	67.02 331	74.40 333	38.86 361	83.27 287	86.19 290	45.11 349	54.27 315	81.15 268	36.91 303	80.01 351	48.79 300	57.02 319	82.19 311

test20.03		63.83 301	62.65 297	67.38 330	70.58 346	39.94 357	86.57 269	84.17 306	63.29 274	51.86 324	77.30 302	37.09 301	82.47 342	38.87 342	54.13 329	79.73 329

MDA-MVSNet_test_wron		63.78 302	60.16 305	74.64 292	78.15 316	60.41 250	83.49 283	84.03 307	56.17 323	39.17 357	71.59 333	37.22 298	83.24 339	42.87 327	48.73 339	80.26 326

YYNet1		63.76 303	60.14 306	74.62 293	78.06 317	60.19 255	83.46 285	83.99 311	56.18 322	39.25 356	71.56 334	37.18 299	83.34 337	42.90 326	48.70 340	80.32 325

K. test v3		63.09 304	59.61 308	73.53 302	76.26 327	49.38 332	83.27 287	77.15 332	64.35 265	47.77 340	72.32 329	28.73 330	87.79 311	49.93 295	36.69 358	83.41 291

COLMAP_ROB	57.96 20	62.98 305	59.65 307	72.98 306	81.44 277	53.00 314	83.75 281	75.53 338	48.34 343	48.81 337	81.40 261	24.14 340	90.30 284	32.95 355	60.52 308	75.65 346
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 306	59.08 309	71.10 318	67.19 352	48.72 334	83.91 280	85.23 298	50.38 337	47.84 339	71.22 336	20.74 347	85.51 325	46.47 312	58.75 316	79.06 334

AllTest		61.66 307	58.06 311	72.46 310	79.57 294	51.42 322	80.17 314	68.61 353	51.25 334	45.88 342	81.23 263	19.86 351	86.58 321	38.98 340	57.01 320	79.39 331

UnsupCasMVSNet_bld		61.60 308	57.71 312	73.29 304	68.73 350	51.64 319	78.61 321	89.05 245	57.20 316	46.11 341	61.96 352	28.70 331	88.60 300	50.08 294	38.90 356	79.63 330

MDA-MVSNet-bldmvs		61.54 309	57.70 313	73.05 305	79.53 296	57.00 295	83.08 291	81.23 322	57.57 312	34.91 359	72.45 326	32.79 315	86.26 323	35.81 347	41.95 350	75.89 345

KD-MVS_self_test		60.87 310	58.60 310	67.68 328	66.13 354	39.93 358	75.63 333	84.70 302	57.32 315	49.57 334	68.45 340	29.55 327	82.87 340	48.09 302	47.94 341	80.25 327

TinyColmap		60.32 311	56.42 318	72.00 316	78.78 308	53.18 313	78.36 324	75.64 336	52.30 330	41.59 355	75.82 317	14.76 358	88.35 304	35.84 346	54.71 328	74.46 347

MVS-HIRNet		60.25 312	55.55 319	74.35 295	84.37 248	56.57 297	71.64 337	74.11 341	34.44 358	45.54 346	42.24 365	31.11 325	89.81 293	40.36 337	76.10 195	76.67 344

MIMVSNet1		60.16 313	57.33 314	68.67 324	69.71 347	44.13 350	78.92 320	84.21 305	55.05 325	44.63 349	71.85 331	23.91 341	81.54 348	32.63 357	55.03 326	80.35 324

PM-MVS		59.40 314	56.59 316	67.84 326	63.63 356	41.86 352	76.76 329	63.22 360	59.01 308	51.07 329	72.27 330	11.72 361	83.25 338	61.34 250	50.28 338	78.39 339

new-patchmatchnet		59.30 315	56.48 317	67.79 327	65.86 355	44.19 349	82.47 295	81.77 320	59.94 303	43.65 352	66.20 344	27.67 333	81.68 347	39.34 339	41.40 351	77.50 342

test_vis1_rt		59.09 316	57.31 315	64.43 333	68.44 351	46.02 346	83.05 292	48.63 370	51.96 332	49.57 334	63.86 348	16.30 353	80.20 350	71.21 165	62.79 285	67.07 357

test_fmvs3		56.82 317	54.86 320	62.69 335	53.59 364	35.47 363	75.87 331	65.64 358	43.91 352	55.10 312	71.43 335	6.91 369	74.40 356	68.64 191	52.63 331	78.20 340

DSMNet-mixed		56.78 318	54.44 321	63.79 334	63.21 357	29.44 372	64.43 352	64.10 359	42.12 355	51.32 327	71.60 332	31.76 320	75.04 354	36.23 345	65.20 267	86.87 237

pmmvs3		55.51 319	51.50 324	67.53 329	57.90 362	50.93 325	80.37 310	73.66 342	40.63 356	44.15 351	64.75 347	16.30 353	78.97 352	44.77 321	40.98 354	72.69 349

TDRefinement		55.28 320	51.58 323	66.39 332	59.53 361	46.15 345	76.23 330	72.80 344	44.60 350	42.49 353	76.28 313	15.29 356	82.39 343	33.20 354	43.75 347	70.62 353

LF4IMVS		54.01 321	52.12 322	59.69 336	62.41 359	39.91 359	68.59 344	68.28 355	42.96 354	44.55 350	75.18 318	14.09 360	68.39 360	41.36 333	51.68 334	70.78 352

N_pmnet		50.55 322	49.11 325	54.88 342	77.17 323	4.02 382	84.36 277	2.00 381	48.59 341	45.86 344	68.82 339	32.22 318	82.80 341	31.58 360	51.38 335	77.81 341

new_pmnet		49.31 323	46.44 326	57.93 337	62.84 358	40.74 355	68.47 345	62.96 361	36.48 357	35.09 358	57.81 354	14.97 357	72.18 357	32.86 356	46.44 343	60.88 359

mvsany_test3		48.86 324	46.35 327	56.41 338	46.00 370	31.67 368	62.26 354	47.25 371	43.71 353	45.54 346	68.15 341	10.84 362	64.44 368	57.95 266	35.44 361	73.13 348

test_f		46.58 325	43.45 328	55.96 339	45.18 371	32.05 367	61.18 355	49.49 369	33.39 359	42.05 354	62.48 351	7.00 368	65.56 364	47.08 310	43.21 349	70.27 354

FPMVS		45.64 326	43.10 329	53.23 344	51.42 367	36.46 362	64.97 351	71.91 347	29.13 362	27.53 362	61.55 353	9.83 364	65.01 366	16.00 369	55.58 324	58.22 360

EGC-MVSNET		42.35 327	38.09 330	55.11 341	74.57 331	46.62 344	71.63 338	55.77 364	0.04 376	0.24 377	62.70 350	14.24 359	74.91 355	17.59 366	46.06 344	43.80 362

LCM-MVSNet		40.54 328	35.79 333	54.76 343	36.92 377	30.81 369	51.41 364	69.02 352	22.07 364	24.63 364	45.37 361	4.56 373	65.81 363	33.67 352	34.50 362	67.67 355

APD_test1		40.50 329	37.31 332	50.09 346	51.88 365	35.27 364	59.45 359	52.59 366	21.64 365	26.12 363	57.80 355	4.56 373	66.56 362	22.64 363	39.09 355	48.43 361

test_vis3_rt		40.46 330	37.79 331	48.47 348	44.49 372	33.35 366	66.56 350	32.84 378	32.39 360	29.65 360	39.13 368	3.91 376	68.65 359	50.17 292	40.99 353	43.40 363

ANet_high		40.27 331	35.20 334	55.47 340	34.74 378	34.47 365	63.84 353	71.56 348	48.42 342	18.80 367	41.08 366	9.52 365	64.45 367	20.18 364	8.66 374	67.49 356

test_method		38.59 332	35.16 335	48.89 347	54.33 363	21.35 377	45.32 367	53.71 365	7.41 373	28.74 361	51.62 357	8.70 366	52.87 371	33.73 351	32.89 363	72.47 350

PMMVS2		37.93 333	33.61 336	50.92 345	46.31 369	24.76 375	60.55 358	50.05 367	28.94 363	20.93 365	47.59 358	4.41 375	65.13 365	25.14 361	18.55 369	62.87 358

Gipuma		34.91 334	31.44 337	45.30 349	70.99 343	39.64 360	19.85 371	72.56 345	20.10 367	16.16 371	21.47 372	5.08 372	71.16 358	13.07 370	43.70 348	25.08 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 335	29.47 338	42.67 351	41.89 374	30.81 369	52.07 362	43.45 372	15.45 368	18.52 368	44.82 362	2.12 377	58.38 369	16.05 367	30.87 365	38.83 364

APD_test2		32.77 335	29.47 338	42.67 351	41.89 374	30.81 369	52.07 362	43.45 372	15.45 368	18.52 368	44.82 362	2.12 377	58.38 369	16.05 367	30.87 365	38.83 364

PMVS	26.43 22	31.84 337	28.16 340	42.89 350	25.87 380	27.58 373	50.92 365	49.78 368	21.37 366	14.17 372	40.81 367	2.01 379	66.62 361	9.61 372	38.88 357	34.49 368
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 338	24.00 342	26.45 355	43.74 373	18.44 379	60.86 356	39.66 374	15.11 370	9.53 374	22.10 371	6.52 370	46.94 373	8.31 373	10.14 371	13.98 371

MVE	24.84 23	24.35 339	19.77 345	38.09 353	34.56 379	26.92 374	26.57 369	38.87 376	11.73 372	11.37 373	27.44 369	1.37 380	50.42 372	11.41 371	14.60 370	36.93 366
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 340	23.20 344	25.46 356	41.52 376	16.90 380	60.56 357	38.79 377	14.62 371	8.99 375	20.24 374	7.35 367	45.82 374	7.25 374	9.46 372	13.64 372

tmp_tt		22.26 341	23.75 343	17.80 357	5.23 381	12.06 381	35.26 368	39.48 375	2.82 375	18.94 366	44.20 364	22.23 345	24.64 376	36.30 344	9.31 373	16.69 370

cdsmvs_eth3d_5k		19.86 342	26.47 341	0.00 361	0.00 384	0.00 385	0.00 372	93.45 75	0.00 379	0.00 380	95.27 43	49.56 225	0.00 380	0.00 378	0.00 377	0.00 376

wuyk23d		11.30 343	10.95 346	12.33 358	48.05 368	19.89 378	25.89 370	1.92 382	3.58 374	3.12 376	1.37 376	0.64 381	15.77 377	6.23 375	7.77 375	1.35 373

ab-mvs-re		7.91 344	10.55 347	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	94.95 51	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

testmvs		7.23 345	9.62 348	0.06 360	0.04 382	0.02 384	84.98 275	0.02 383	0.03 377	0.18 378	1.21 377	0.01 383	0.02 378	0.14 376	0.01 376	0.13 375

test123		6.92 346	9.21 349	0.08 359	0.03 383	0.05 383	81.65 300	0.01 384	0.02 378	0.14 379	0.85 378	0.03 382	0.02 378	0.12 377	0.00 377	0.16 374

pcd_1.5k_mvsjas		4.46 347	5.95 350	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	53.55 191	0.00 380	0.00 378	0.00 377	0.00 376

test_blank		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

uanet_test		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

DCPMVS		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

sosnet-low-res		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

sosnet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

uncertanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

Regformer		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

uanet		0.00 348	0.00 351	0.00 361	0.00 384	0.00 385	0.00 372	0.00 385	0.00 379	0.00 380	0.00 379	0.00 384	0.00 380	0.00 378	0.00 377	0.00 376

FOURS1					93.95 45	61.77 224	93.96 61	91.92 131	62.14 286	86.57 33

MSC_two_6792asdad				89.60 8	97.31 4	73.22 10		95.05 19					99.07 13	92.01 11	94.77 24	96.51 20

PC_three_1452									80.91 34	94.07 2	96.83 13	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

No_MVS				89.60 8	97.31 4	73.22 10		95.05 19					99.07 13	92.01 11	94.77 24	96.51 20

test_one_0601					96.32 18	69.74 40		94.18 49	71.42 194	90.67 15	96.85 11	74.45 18

eth-test2					0.00 384

eth-test					0.00 384

ZD-MVS					96.63 9	65.50 143		93.50 73	70.74 208	85.26 48	95.19 48	64.92 67	97.29 68	87.51 40	93.01 52

RE-MVS-def			80.48 116		92.02 91	58.56 276	90.90 184	90.45 187	62.76 280	78.89 101	94.46 64	49.30 228		78.77 111	86.77 115	92.28 156

IU-MVS					96.46 11	69.91 35		95.18 14	80.75 35	95.28 1				92.34 8	95.36 13	96.47 24

OPU-MVS				89.97 3	97.52 3	73.15 12	96.89 4				97.00 8	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_241102_TWO								94.41 40	71.65 183	92.07 6	97.21 4	74.58 17	99.11 6	92.34 8	95.36 13	96.59 15

test_241102_ONE					96.45 12	69.38 45		94.44 38	71.65 183	92.11 4	97.05 7	76.79 9	99.11 6

9.14			87.63 22		93.86 47		94.41 48	94.18 49	72.76 149	86.21 35	96.51 17	66.64 50	97.88 41	90.08 23	94.04 35

save fliter					93.84 48	67.89 83	95.05 37	92.66 104	78.19 68

test_0728_THIRD									72.48 154	90.55 16	96.93 9	76.24 11	99.08 11	91.53 15	94.99 16	96.43 25

test_0728_SECOND				88.70 15	96.45 12	70.43 27	96.64 8	94.37 44					99.15 2	91.91 13	94.90 20	96.51 20

test0726					96.40 15	69.99 31	96.76 6	94.33 46	71.92 170	91.89 8	97.11 6	73.77 21

GSMVS																94.68 84

test_part2					96.29 19	68.16 77				90.78 13

sam_mvs1												57.85 139				94.68 84

sam_mvs												54.91 176

ambc				69.61 321	61.38 360	41.35 354	49.07 366	85.86 294		50.18 333	66.40 343	10.16 363	88.14 306	45.73 316	44.20 346	79.32 333

MTGPA								92.23 117

test_post1							78.95 319				20.70 373	53.05 196	91.50 277	60.43 255

test_post											23.01 370	56.49 159	92.67 242

patchmatchnet-post											67.62 342	57.62 142	90.25 285

GG-mvs-BLEND				86.53 62	91.91 98	69.67 43	75.02 334	94.75 26		78.67 108	90.85 141	77.91 7	94.56 178	72.25 155	93.74 41	95.36 56

MTMP							93.77 73	32.52 379

gm-plane-assit					88.42 173	67.04 105			78.62 65		91.83 125		97.37 62	76.57 123

test9_res														89.41 24	94.96 17	95.29 61

TEST9					94.18 41	67.28 97	94.16 51	93.51 71	71.75 181	85.52 43	95.33 38	68.01 41	97.27 72

test_8					94.19 40	67.19 99	94.15 54	93.42 77	71.87 175	85.38 46	95.35 37	68.19 39	96.95 91

agg_prior2														86.41 51	94.75 28	95.33 57

agg_prior					94.16 43	66.97 107		93.31 80		84.49 53			96.75 98

TestCases				72.46 310	79.57 294	51.42 322		68.61 353	51.25 334	45.88 342	81.23 263	19.86 351	86.58 321	38.98 340	57.01 320	79.39 331

test_prior4						67.18 101	93.92 64

test_prior2							95.10 36		75.40 104	85.25 49	95.61 33	67.94 42		87.47 41	94.77 24

test_prior				86.42 65	94.71 35	67.35 96		93.10 90					96.84 96			95.05 72

旧先验2							92.00 140		59.37 307	87.54 28			93.47 219	75.39 130

新几何2							91.41 161

新几何1				84.73 114	92.32 84	64.28 173		91.46 156	59.56 306	79.77 91	92.90 103	56.95 152	96.57 103	63.40 236	92.91 54	93.34 129

旧先验1					91.94 95	60.74 245		91.50 154			94.36 68	65.23 62			91.84 67	94.55 88

无先验							92.71 108	92.61 108	62.03 287				97.01 83	66.63 207		93.97 111

原ACMM2							92.01 137

原ACMM1				84.42 124	93.21 63	64.27 174		93.40 79	65.39 258	79.51 94	92.50 111	58.11 138	96.69 99	65.27 226	93.96 36	92.32 154

test222					89.77 141	61.60 228	89.55 222	89.42 228	56.83 319	77.28 120	92.43 115	52.76 199			91.14 81	93.09 137

testdata2													96.09 116	61.26 251

segment_acmp												65.94 56

testdata				81.34 200	89.02 159	57.72 284		89.84 214	58.65 310	85.32 47	94.09 80	57.03 147	93.28 221	69.34 183	90.56 87	93.03 139

testdata1							89.21 231		77.55 81

test12				87.09 43	94.60 36	68.86 58		92.91 95		82.67 67		65.44 61	97.55 54		93.69 44	94.84 80

plane_prior7					86.94 206	61.51 229

plane_prior6					87.23 201	62.32 215						50.66 215

plane_prior5								91.31 160					95.55 144	76.74 121	78.53 173	88.39 214

plane_prior4											89.14 167

plane_prior3						61.95 222			79.09 56	72.53 169

plane_prior2							93.13 93		78.81 62

plane_prior1					87.15 203

plane_prior						62.42 212	93.85 68		79.38 48						78.80 170

n2								0.00 385

nn								0.00 385

door-mid								66.01 357

lessismore_v0				73.72 301	72.93 338	47.83 337		61.72 362		45.86 344	73.76 322	28.63 332	89.81 293	47.75 308	31.37 364	83.53 287

LGP-MVS_train				79.56 243	84.31 249	59.37 265		89.73 219	69.49 222	64.86 255	88.42 172	38.65 284	94.30 186	72.56 152	72.76 215	85.01 275

test11								93.01 91

door								66.57 356

HQP5-MVS						63.66 190

HQP-NCC					87.54 195		94.06 56		79.80 42	74.18 149

ACMP_Plane					87.54 195		94.06 56		79.80 42	74.18 149

BP-MVS														77.63 118

HQP4-MVS										74.18 149			95.61 139			88.63 207

HQP3-MVS								91.70 146							78.90 168

HQP2-MVS												51.63 209

NP-MVS					87.41 198	63.04 199					90.30 151

MDTV_nov1_ep13_2view						59.90 258	80.13 315		67.65 242	72.79 164		54.33 184		59.83 259		92.58 148

MDTV_nov1_ep13			72.61 227		89.06 158	68.48 66	80.33 311	90.11 205	71.84 177	71.81 180	75.92 316	53.01 197	93.92 208	48.04 303	73.38 209

ACMMP++_ref															71.63 223

ACMMP++															69.72 234

Test By Simon												54.21 185

ITE_SJBPF				70.43 320	74.44 332	47.06 343		77.32 331	60.16 301	54.04 317	83.53 232	23.30 343	84.01 331	43.07 324	61.58 301	80.21 328

DeepMVS_CX				34.71 354	51.45 366	24.73 376		28.48 380	31.46 361	17.49 370	52.75 356	5.80 371	42.60 375	18.18 365	19.42 368	36.81 367

High-resolution multi-view benchmark