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.

MSP-MVS		90.38 4	91.87 1	85.88 88	92.83 83	64.03 194	93.06 105	94.33 51	82.19 21	93.65 3	96.15 30	85.89 1	97.19 84	91.02 20	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
MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 44	96.26 27	72.84 27	99.38 1	92.64 6	95.93 10	97.08 9

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 132	93.00 81	58.16 298	96.72 7	94.41 45	86.50 6	90.25 18	97.83 1	75.46 14	98.67 23	92.78 5	95.49 13	97.32 6

DVP-MVS++		90.53 3	91.09 4	88.87 14	97.31 4	69.91 37	93.96 69	94.37 49	72.48 165	92.07 6	96.85 12	83.82 2	99.15 2	91.53 16	97.42 4	97.55 4

patch_mono-2		89.71 10	90.99 5	85.85 91	96.04 26	63.70 204	95.04 41	95.19 13	86.74 5	91.53 11	95.15 59	73.86 21	97.58 63	93.38 3	92.00 75	96.28 32

CNVR-MVS		90.32 5	90.89 6	88.61 19	96.76 10	70.65 26	96.47 12	94.83 26	84.83 9	89.07 23	96.80 15	70.86 35	99.06 15	92.64 6	95.71 11	96.12 35

DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 162	76.68 2	97.29 1	95.35 10	82.87 15	91.58 10	97.22 4	79.93 5	99.10 9	83.12 82	97.64 2	97.94 1

ETH3 D test6400		90.27 6	90.44 8	89.75 6	96.82 9	74.33 7	95.89 16	94.80 29	77.13 89	89.13 22	97.38 2	74.49 18	98.48 28	92.32 11	95.98 8	96.46 25

SED-MVS		89.94 8	90.36 9	88.70 16	96.45 14	69.38 47	96.89 4	94.44 43	71.65 195	92.11 4	97.21 5	76.79 9	99.11 6	92.34 8	95.36 14	97.62 2

DELS-MVS		90.05 7	90.09 10	89.94 4	93.14 78	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 69	74.11 20	98.91 17	87.26 51	95.94 9	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
CANet		89.61 11	89.99 11	88.46 20	94.39 44	69.71 43	96.53 11	93.78 64	86.89 4	89.68 19	95.78 34	65.94 64	99.10 9	92.99 4	93.91 44	96.58 17

HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 34	68.23 77	95.24 32	94.49 41	82.43 19	88.90 24	96.35 24	71.89 34	98.63 24	88.76 36	96.40 6	96.06 36

DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 17	69.99 33	96.64 8	94.52 39	71.92 181	90.55 16	96.93 10	73.77 22	99.08 11	91.91 14	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
NCCC		89.07 14	89.46 14	87.91 24	96.60 12	69.05 55	96.38 13	94.64 36	84.42 10	86.74 34	96.20 28	66.56 60	98.76 22	89.03 34	94.56 33	95.92 41

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 22	67.56 93	94.17 55	94.15 57	68.77 250	90.74 14	97.27 3	76.09 12	98.49 27	90.58 23	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
TSAR-MVS + MP.		88.11 18	88.64 16	86.54 64	91.73 118	68.04 81	90.36 216	93.55 77	82.89 14	91.29 12	92.89 120	72.27 31	96.03 139	87.99 40	94.77 25	95.54 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPNet		87.84 22	88.38 17	86.23 78	93.30 71	66.05 137	95.26 31	94.84 25	87.09 3	88.06 26	94.53 77	66.79 57	97.34 75	83.89 78	91.68 80	95.29 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TSAR-MVS + GP.		87.96 19	88.37 18	86.70 58	93.51 67	65.32 156	95.15 35	93.84 63	78.17 73	85.93 43	94.80 72	75.80 13	98.21 35	89.38 27	88.78 108	96.59 15

SMA-MVS		88.14 16	88.29 19	87.67 28	93.21 75	68.72 63	93.85 79	94.03 60	74.18 127	91.74 9	96.67 16	65.61 69	98.42 32	89.24 30	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
DeepC-MVS_fast	79.48 2	87.95 20	88.00 20	87.79 27	95.86 29	68.32 71	95.74 20	94.11 59	83.82 12	83.49 69	96.19 29	64.53 81	98.44 30	83.42 81	94.88 24	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
ETH3D-3000-0.1		87.61 24	87.89 21	86.75 55	93.58 64	67.21 106	94.31 53	94.14 58	72.92 156	87.13 30	96.62 17	67.81 49	97.94 43	90.13 24	94.42 36	95.09 74

APDe-MVS		87.54 25	87.84 22	86.65 59	96.07 25	66.30 132	94.84 46	93.78 64	69.35 241	88.39 25	96.34 25	67.74 50	97.66 58	90.62 22	93.44 53	96.01 39

lupinMVS		87.74 23	87.77 23	87.63 33	89.24 175	71.18 21	96.57 10	92.90 107	82.70 18	87.13 30	95.27 52	64.99 74	95.80 144	89.34 28	91.80 78	95.93 40

test_prior3		87.38 27	87.70 24	86.42 69	94.71 39	67.35 102	95.10 37	93.10 100	75.40 110	85.25 54	95.61 40	67.94 45	96.84 111	87.47 46	94.77 25	95.05 76

9.14			87.63 25		93.86 55		94.41 51	94.18 55	72.76 159	86.21 38	96.51 19	66.64 58	97.88 49	90.08 25	94.04 41

PS-MVSNAJ		88.14 16	87.61 26	89.71 7	92.06 104	76.72 1	95.75 19	93.26 90	83.86 11	89.55 20	96.06 31	53.55 205	97.89 48	91.10 18	93.31 55	94.54 96

Regformer-1		87.24 30	87.60 27	86.15 80	95.14 32	65.83 145	93.95 72	95.12 16	82.11 22	84.25 61	95.73 36	67.88 48	98.35 33	85.60 63	88.64 111	94.26 105

dcpmvs_2		87.37 28	87.55 28	86.85 49	95.04 36	68.20 78	90.36 216	90.66 200	79.37 52	81.20 86	93.67 102	74.73 15	96.55 123	90.88 21	92.00 75	95.82 44

SD-MVS		87.49 26	87.49 29	87.50 35	93.60 63	68.82 61	93.90 76	92.63 118	76.86 92	87.90 27	95.76 35	66.17 61	97.63 60	89.06 32	91.48 84	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
Regformer-2		87.00 37	87.43 30	85.71 98	95.14 32	64.73 173	93.95 72	94.95 23	81.69 27	84.03 66	95.73 36	67.35 52	98.19 37	85.40 65	88.64 111	94.20 107

train_agg		87.21 31	87.42 31	86.60 60	94.18 46	67.28 104	94.16 56	93.51 78	71.87 186	85.52 48	95.33 47	68.19 41	97.27 82	89.09 31	94.90 21	95.25 69

xiu_mvs_v2_base		87.92 21	87.38 32	89.55 12	91.41 129	76.43 3	95.74 20	93.12 99	83.53 13	89.55 20	95.95 32	53.45 209	97.68 54	91.07 19	92.62 64	94.54 96

agg_prior1		87.02 36	87.26 33	86.28 77	94.16 50	66.97 115	94.08 63	93.31 88	71.85 188	84.49 59	95.39 45	68.91 38	96.75 116	88.84 35	94.32 38	95.13 72

xxxxxxxxxxxxxcwj		87.14 32	87.19 34	86.99 47	93.84 56	67.89 85	95.05 39	84.72 321	78.19 71	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

ETH3D cwj APD-0.16		87.06 34	87.18 35	86.71 56	91.99 109	67.48 100	92.97 110	94.21 54	71.48 206	85.72 45	96.32 26	68.13 43	98.00 42	89.06 32	94.70 31	94.65 92

SF-MVS		87.03 35	87.09 36	86.84 50	92.70 89	67.45 101	93.64 87	93.76 67	70.78 223	86.25 36	96.44 21	66.98 54	97.79 51	88.68 37	94.56 33	95.28 64

CS-MVS-test		86.14 49	87.01 37	83.52 165	92.63 92	59.36 285	95.49 26	91.92 143	80.09 42	85.46 50	95.53 43	61.82 113	95.77 147	86.77 56	93.37 54	95.41 53

alignmvs		87.28 29	86.97 38	88.24 23	91.30 130	71.14 23	95.61 24	93.56 76	79.30 53	87.07 33	95.25 54	68.43 39	96.93 109	87.87 41	84.33 148	96.65 13

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 62	90.42 146	66.38 129	96.09 15	93.87 62	77.73 80	84.01 67	95.66 38	63.39 97	97.94 43	87.40 48	93.55 52	95.42 52
Skip Steuart: Steuart Systems R&D Blog.
PVSNet_Blended		86.73 42	86.86 40	86.31 76	93.76 58	67.53 96	96.33 14	93.61 74	82.34 20	81.00 91	93.08 112	63.19 100	97.29 78	87.08 52	91.38 86	94.13 113

PHI-MVS		86.83 40	86.85 41	86.78 54	93.47 68	65.55 152	95.39 29	95.10 18	71.77 192	85.69 47	96.52 18	62.07 109	98.77 21	86.06 61	95.60 12	96.03 38

testtj		86.62 43	86.66 42	86.50 66	96.95 8	65.70 147	94.41 51	93.45 82	67.74 257	86.19 39	96.39 23	64.38 82	97.91 46	87.33 49	93.14 58	95.90 42

CS-MVS		85.80 54	86.65 43	83.27 173	92.00 108	58.92 290	95.31 30	91.86 149	79.97 43	84.82 56	95.40 44	62.26 107	95.51 166	86.11 60	92.08 74	95.37 56

MG-MVS		87.11 33	86.27 44	89.62 8	97.79 1	76.27 4	94.96 44	94.49 41	78.74 67	83.87 68	92.94 117	64.34 83	96.94 107	75.19 141	94.09 40	95.66 47

CSCG		86.87 38	86.26 45	88.72 15	95.05 35	70.79 25	93.83 83	95.33 11	68.48 254	77.63 128	94.35 86	73.04 25	98.45 29	84.92 70	93.71 49	96.92 11

canonicalmvs		86.85 39	86.25 46	88.66 18	91.80 117	71.92 15	93.54 92	91.71 157	80.26 41	87.55 28	95.25 54	63.59 95	96.93 109	88.18 39	84.34 147	97.11 8

jason		86.40 44	86.17 47	87.11 43	86.16 239	70.54 28	95.71 23	92.19 135	82.00 24	84.58 58	94.34 87	61.86 111	95.53 165	87.76 42	90.89 92	95.27 66
jason: jason.
ETV-MVS		86.01 51	86.11 48	85.70 99	90.21 151	67.02 114	93.43 97	91.92 143	81.21 32	84.13 65	94.07 96	60.93 120	95.63 155	89.28 29	89.81 100	94.46 103

APD-MVS		85.93 52	85.99 49	85.76 95	95.98 28	65.21 159	93.59 90	92.58 120	66.54 269	86.17 40	95.88 33	63.83 89	97.00 99	86.39 58	92.94 60	95.06 75
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
Regformer-3		85.80 54	85.92 50	85.46 105	94.17 48	65.09 166	92.95 112	95.11 17	81.13 33	81.68 81	95.04 60	65.82 66	98.32 34	83.02 83	84.36 145	92.97 156

MSLP-MVS++		86.27 46	85.91 51	87.35 38	92.01 107	68.97 58	95.04 41	92.70 112	79.04 61	81.50 83	96.50 20	58.98 144	96.78 113	83.49 80	93.93 43	96.29 30

WTY-MVS		86.32 45	85.81 52	87.85 25	92.82 85	69.37 49	95.20 33	95.25 12	82.71 17	81.91 79	94.73 73	67.93 47	97.63 60	79.55 111	82.25 159	96.54 18

ACMMP_NAP		86.05 50	85.80 53	86.80 53	91.58 122	67.53 96	91.79 161	93.49 81	74.93 118	84.61 57	95.30 49	59.42 137	97.92 45	86.13 59	94.92 19	94.94 81

MVS_111021_HR		86.19 48	85.80 53	87.37 37	93.17 77	69.79 40	93.99 68	93.76 67	79.08 60	78.88 117	93.99 97	62.25 108	98.15 38	85.93 62	91.15 90	94.15 112

Regformer-4		85.45 58	85.69 55	84.73 130	94.17 48	63.23 213	92.95 112	94.83 26	80.66 38	81.29 84	95.04 60	65.12 72	98.08 40	82.74 85	84.36 145	92.88 160

VNet		86.20 47	85.65 56	87.84 26	93.92 54	69.99 33	95.73 22	95.94 6	78.43 69	86.00 42	93.07 114	58.22 148	97.00 99	85.22 67	84.33 148	96.52 19

CDPH-MVS		85.71 56	85.46 57	86.46 67	94.75 38	67.19 107	93.89 77	92.83 109	70.90 218	83.09 72	95.28 50	63.62 93	97.36 73	80.63 105	94.18 39	94.84 85

PAPM		85.89 53	85.46 57	87.18 41	88.20 202	72.42 14	92.41 135	92.77 110	82.11 22	80.34 97	93.07 114	68.27 40	95.02 178	78.39 124	93.59 51	94.09 115

DeepC-MVS	77.85 3	85.52 57	85.24 59	86.37 72	88.80 185	66.64 123	92.15 141	93.68 72	81.07 34	76.91 138	93.64 103	62.59 105	98.44 30	85.50 64	92.84 62	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
MP-MVS-pluss		85.24 61	85.13 60	85.56 102	91.42 127	65.59 151	91.54 173	92.51 122	74.56 121	80.62 94	95.64 39	59.15 141	97.00 99	86.94 54	93.80 45	94.07 117
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ZNCC-MVS		85.33 60	85.08 61	86.06 81	93.09 80	65.65 149	93.89 77	93.41 86	73.75 138	79.94 101	94.68 75	60.61 123	98.03 41	82.63 88	93.72 48	94.52 98

DROMVSNet		84.53 72	85.04 62	83.01 177	89.34 169	61.37 250	94.42 50	91.09 186	77.91 77	83.24 70	94.20 92	58.37 147	95.40 167	85.35 66	91.41 85	92.27 177

MP-MVS		85.02 63	84.97 63	85.17 117	92.60 93	64.27 190	93.24 100	92.27 128	73.13 150	79.63 106	94.43 80	61.90 110	97.17 85	85.00 68	92.56 65	94.06 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EIA-MVS		84.84 67	84.88 64	84.69 133	91.30 130	62.36 232	93.85 79	92.04 138	79.45 50	79.33 109	94.28 90	62.42 106	96.35 127	80.05 108	91.25 89	95.38 55

casdiffmvs		85.37 59	84.87 65	86.84 50	88.25 200	69.07 54	93.04 107	91.76 154	81.27 31	80.84 93	92.07 138	64.23 84	96.06 137	84.98 69	87.43 121	95.39 54

#test#		84.98 65	84.74 66	85.72 96	93.75 60	65.01 167	94.09 62	93.19 94	73.55 144	79.22 110	94.93 66	59.04 142	97.67 55	82.66 86	92.21 69	94.49 101

zzz-MVS		84.73 68	84.47 67	85.50 103	91.89 114	65.16 161	91.55 172	92.23 129	75.32 112	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 108

PAPR		85.15 62	84.47 67	87.18 41	96.02 27	68.29 73	91.85 159	93.00 104	76.59 97	79.03 113	95.00 62	61.59 114	97.61 62	78.16 125	89.00 107	95.63 48

baseline		85.01 64	84.44 69	86.71 56	88.33 197	68.73 62	90.24 221	91.82 153	81.05 35	81.18 87	92.50 127	63.69 92	96.08 136	84.45 73	86.71 129	95.32 60

HFP-MVS		84.73 68	84.40 70	85.72 96	93.75 60	65.01 167	93.50 94	93.19 94	72.19 175	79.22 110	94.93 66	59.04 142	97.67 55	81.55 96	92.21 69	94.49 101

GST-MVS		84.63 71	84.29 71	85.66 100	92.82 85	65.27 157	93.04 107	93.13 98	73.20 148	78.89 114	94.18 93	59.41 138	97.85 50	81.45 98	92.48 68	93.86 127

ACMMPR		84.37 73	84.06 72	85.28 113	93.56 65	64.37 185	93.50 94	93.15 97	72.19 175	78.85 119	94.86 70	56.69 169	97.45 67	81.55 96	92.20 71	94.02 120

region2R		84.36 74	84.03 73	85.36 110	93.54 66	64.31 187	93.43 97	92.95 105	72.16 178	78.86 118	94.84 71	56.97 164	97.53 65	81.38 100	92.11 73	94.24 106

diffmvs		84.28 76	83.83 74	85.61 101	87.40 219	68.02 82	90.88 201	89.24 251	80.54 39	81.64 82	92.52 126	59.83 132	94.52 201	87.32 50	85.11 139	94.29 104

EI-MVSNet-Vis-set		83.77 88	83.67 75	84.06 152	92.79 88	63.56 209	91.76 164	94.81 28	79.65 49	77.87 125	94.09 94	63.35 98	97.90 47	79.35 113	79.36 183	90.74 201

CANet_DTU		84.09 82	83.52 76	85.81 92	90.30 149	66.82 118	91.87 157	89.01 264	85.27 7	86.09 41	93.74 101	47.71 263	96.98 103	77.90 128	89.78 102	93.65 133

PVSNet_Blended_VisFu		83.97 84	83.50 77	85.39 109	90.02 154	66.59 126	93.77 84	91.73 155	77.43 88	77.08 137	89.81 173	63.77 91	96.97 104	79.67 110	88.21 114	92.60 165

XVS		83.87 86	83.47 78	85.05 118	93.22 73	63.78 199	92.92 115	92.66 115	73.99 130	78.18 123	94.31 89	55.25 183	97.41 70	79.16 115	91.58 82	93.95 122

CHOSEN 1792x2688		84.98 65	83.45 79	89.57 11	89.94 157	75.14 5	92.07 147	92.32 126	81.87 25	75.68 147	88.27 190	60.18 126	98.60 25	80.46 107	90.27 99	94.96 80

PVSNet_BlendedMVS		83.38 93	83.43 80	83.22 174	93.76 58	67.53 96	94.06 64	93.61 74	79.13 58	81.00 91	85.14 225	63.19 100	97.29 78	87.08 52	73.91 224	84.83 293

MAR-MVS		84.18 80	83.43 80	86.44 68	96.25 23	65.93 142	94.28 54	94.27 53	74.41 122	79.16 112	95.61 40	53.99 200	98.88 20	69.62 190	93.26 56	94.50 100
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
baseline2		83.68 91	83.42 82	84.48 140	87.37 220	66.00 139	90.06 225	95.93 7	79.71 48	69.08 226	90.39 163	77.92 6	96.28 128	78.91 119	81.38 169	91.16 197

CP-MVS		83.71 90	83.40 83	84.65 134	93.14 78	63.84 197	94.59 48	92.28 127	71.03 215	77.41 131	94.92 68	55.21 186	96.19 130	81.32 101	90.70 94	93.91 124

MTAPA		83.91 85	83.38 84	85.50 103	91.89 114	65.16 161	81.75 309	92.23 129	75.32 112	80.53 95	95.21 56	56.06 177	97.16 86	84.86 71	92.55 66	94.18 108

HY-MVS	76.49 5	84.28 76	83.36 85	87.02 46	92.22 101	67.74 89	84.65 289	94.50 40	79.15 57	82.23 77	87.93 197	66.88 56	96.94 107	80.53 106	82.20 160	96.39 28

MVS_Test		84.16 81	83.20 86	87.05 45	91.56 123	69.82 39	89.99 230	92.05 137	77.77 79	82.84 73	86.57 211	63.93 88	96.09 134	74.91 147	89.18 106	95.25 69

test_yl		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 244	81.09 88	92.88 121	57.00 162	97.44 68	81.11 103	81.76 163	96.23 33

DCV-MVSNet		84.28 76	83.16 87	87.64 29	94.52 42	69.24 50	95.78 17	95.09 19	69.19 244	81.09 88	92.88 121	57.00 162	97.44 68	81.11 103	81.76 163	96.23 33

ET-MVSNet_ETH3D		84.01 83	83.15 89	86.58 62	90.78 141	70.89 24	94.74 47	94.62 37	81.44 30	58.19 315	93.64 103	73.64 24	92.35 276	82.66 86	78.66 191	96.50 23

EI-MVSNet-UG-set		83.14 98	82.96 90	83.67 163	92.28 99	63.19 215	91.38 182	94.68 34	79.22 55	76.60 140	93.75 100	62.64 104	97.76 53	78.07 126	78.01 194	90.05 209

HPM-MVS		83.25 96	82.95 91	84.17 149	92.25 100	62.88 225	90.91 198	91.86 149	70.30 229	77.12 135	93.96 98	56.75 167	96.28 128	82.04 92	91.34 88	93.34 142
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test2506		83.29 94	82.92 92	84.37 143	88.39 195	63.18 216	92.01 150	91.35 174	77.66 82	78.49 122	91.42 146	64.58 80	95.09 176	73.19 153	89.23 104	94.85 82

MVSFormer		83.75 89	82.88 93	86.37 72	89.24 175	71.18 21	89.07 249	90.69 197	65.80 274	87.13 30	94.34 87	64.99 74	92.67 262	72.83 157	91.80 78	95.27 66

MVS		84.66 70	82.86 94	90.06 2	90.93 136	74.56 6	87.91 265	95.54 9	68.55 252	72.35 192	94.71 74	59.78 133	98.90 18	81.29 102	94.69 32	96.74 12

Effi-MVS+		83.82 87	82.76 95	86.99 47	89.56 165	69.40 46	91.35 184	86.12 310	72.59 161	83.22 71	92.81 124	59.60 135	96.01 141	81.76 94	87.80 118	95.56 50

LFMVS		84.34 75	82.73 96	89.18 13	94.76 37	73.25 10	94.99 43	91.89 148	71.90 183	82.16 78	93.49 107	47.98 260	97.05 90	82.55 89	84.82 141	97.25 7

iter_conf05		83.27 95	82.70 97	84.98 121	93.32 70	71.84 16	94.16 56	81.76 341	82.74 16	73.83 172	88.40 186	72.77 28	94.61 192	82.10 91	75.21 217	88.48 227

PGM-MVS		83.25 96	82.70 97	84.92 123	92.81 87	64.07 193	90.44 212	92.20 134	71.28 208	77.23 134	94.43 80	55.17 187	97.31 77	79.33 114	91.38 86	93.37 141

SR-MVS		82.81 103	82.58 99	83.50 168	93.35 69	61.16 253	92.23 140	91.28 178	64.48 282	81.27 85	95.28 50	53.71 204	95.86 143	82.87 84	88.77 109	93.49 138

h-mvs33		83.01 100	82.56 100	84.35 144	89.34 169	62.02 237	92.72 120	93.76 67	81.45 28	82.73 74	92.25 136	60.11 127	97.13 88	87.69 43	62.96 298	93.91 124

thisisatest0515		83.41 92	82.49 101	86.16 79	89.46 168	68.26 75	93.54 92	94.70 33	74.31 125	75.75 145	90.92 152	72.62 29	96.52 124	69.64 188	81.50 168	93.71 131

mPP-MVS		82.96 102	82.44 102	84.52 138	92.83 83	62.92 223	92.76 118	91.85 151	71.52 203	75.61 150	94.24 91	53.48 208	96.99 102	78.97 118	90.73 93	93.64 134

sss		82.71 106	82.38 103	83.73 160	89.25 174	59.58 280	92.24 139	94.89 24	77.96 75	79.86 103	92.38 132	56.70 168	97.05 90	77.26 131	80.86 174	94.55 94

CLD-MVS		82.73 104	82.35 104	83.86 156	87.90 209	67.65 92	95.45 27	92.18 136	85.06 8	72.58 185	92.27 135	52.46 219	95.78 145	84.18 74	79.06 186	88.16 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSTER		82.47 108	82.05 105	83.74 158	92.68 90	69.01 56	91.90 156	93.21 91	79.83 44	72.14 193	85.71 222	74.72 16	94.72 188	75.72 138	72.49 235	87.50 239

PMMVS		81.98 118	82.04 106	81.78 210	89.76 161	56.17 318	91.13 194	90.69 197	77.96 75	80.09 100	93.57 105	46.33 274	94.99 179	81.41 99	87.46 120	94.17 110

TESTMET0.1,1		82.41 109	81.98 107	83.72 161	88.08 203	63.74 201	92.70 122	93.77 66	79.30 53	77.61 129	87.57 202	58.19 149	94.08 216	73.91 152	86.68 130	93.33 144

PAPM_NR		82.97 101	81.84 108	86.37 72	94.10 52	66.76 121	87.66 270	92.84 108	69.96 233	74.07 169	93.57 105	63.10 102	97.50 66	70.66 181	90.58 96	94.85 82

test1172		81.90 119	81.83 109	82.13 202	93.23 72	57.52 308	91.61 171	90.98 193	64.32 284	80.20 99	95.00 62	51.26 229	95.61 157	81.73 95	88.13 115	93.26 146

VDD-MVS		83.06 99	81.81 110	86.81 52	90.86 139	67.70 90	95.40 28	91.50 168	75.46 107	81.78 80	92.34 134	40.09 298	97.13 88	86.85 55	82.04 161	95.60 49

DP-MVS Recon		82.73 104	81.65 111	85.98 85	97.31 4	67.06 111	95.15 35	91.99 140	69.08 247	76.50 142	93.89 99	54.48 195	98.20 36	70.76 179	85.66 136	92.69 162

MVS_111021_LR		82.02 117	81.52 112	83.51 167	88.42 193	62.88 225	89.77 234	88.93 266	76.78 95	75.55 151	93.10 110	50.31 236	95.38 169	83.82 79	87.02 123	92.26 178

EPP-MVSNet		81.79 121	81.52 112	82.61 186	88.77 186	60.21 272	93.02 109	93.66 73	68.52 253	72.90 180	90.39 163	72.19 32	94.96 180	74.93 146	79.29 185	92.67 163

APD-MVS_3200maxsize		81.64 123	81.32 114	82.59 187	92.36 96	58.74 292	91.39 180	91.01 192	63.35 290	79.72 105	94.62 76	51.82 222	96.14 132	79.71 109	87.93 117	92.89 159

CostFormer		82.33 110	81.15 115	85.86 90	89.01 180	68.46 68	82.39 307	93.01 102	75.59 105	80.25 98	81.57 273	72.03 33	94.96 180	79.06 117	77.48 203	94.16 111

xiu_mvs_v1_base_debu		82.16 113	81.12 116	85.26 114	86.42 233	68.72 63	92.59 130	90.44 207	73.12 151	84.20 62	94.36 82	38.04 310	95.73 149	84.12 75	86.81 124	91.33 191

xiu_mvs_v1_base		82.16 113	81.12 116	85.26 114	86.42 233	68.72 63	92.59 130	90.44 207	73.12 151	84.20 62	94.36 82	38.04 310	95.73 149	84.12 75	86.81 124	91.33 191

xiu_mvs_v1_base_debi		82.16 113	81.12 116	85.26 114	86.42 233	68.72 63	92.59 130	90.44 207	73.12 151	84.20 62	94.36 82	38.04 310	95.73 149	84.12 75	86.81 124	91.33 191

hse-mvs2		81.12 132	81.11 119	81.16 224	86.52 232	57.48 309	89.40 242	91.16 181	81.45 28	82.73 74	90.49 161	60.11 127	94.58 194	87.69 43	60.41 324	91.41 190

baseline1		81.84 120	81.03 120	84.28 147	91.60 121	66.62 124	91.08 195	91.66 162	81.87 25	74.86 155	91.67 145	69.98 37	94.92 183	71.76 172	64.75 287	91.29 196

iter_conf_final		81.74 122	80.93 121	84.18 148	92.66 91	69.10 53	92.94 114	82.80 339	79.01 62	74.85 156	88.40 186	61.83 112	94.61 192	79.36 112	76.52 212	88.83 219

3Dnovator	73.91 6	82.69 107	80.82 122	88.31 22	89.57 164	71.26 20	92.60 128	94.39 48	78.84 64	67.89 245	92.48 130	48.42 255	98.52 26	68.80 200	94.40 37	95.15 71

CDS-MVSNet		81.43 125	80.74 123	83.52 165	86.26 237	64.45 179	92.09 145	90.65 201	75.83 104	73.95 171	89.81 173	63.97 87	92.91 252	71.27 175	82.82 156	93.20 149
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SR-MVS-dyc-post		81.06 133	80.70 124	82.15 200	92.02 105	58.56 294	90.90 199	90.45 204	62.76 296	78.89 114	94.46 78	51.26 229	95.61 157	78.77 121	86.77 127	92.28 174

ACMMP		81.49 124	80.67 125	83.93 155	91.71 119	62.90 224	92.13 142	92.22 133	71.79 191	71.68 200	93.49 107	50.32 235	96.96 105	78.47 123	84.22 152	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
HQP-MVS		81.14 130	80.64 126	82.64 185	87.54 215	63.66 207	94.06 64	91.70 160	79.80 45	74.18 165	90.30 165	51.63 226	95.61 157	77.63 129	78.90 187	88.63 224

3Dnovator+	73.60 7	82.10 116	80.60 127	86.60 60	90.89 138	66.80 120	95.20 33	93.44 84	74.05 129	67.42 251	92.49 129	49.46 245	97.65 59	70.80 178	91.68 80	95.33 58

API-MVS		82.28 111	80.53 128	87.54 34	96.13 24	70.59 27	93.63 88	91.04 191	65.72 276	75.45 152	92.83 123	56.11 176	98.89 19	64.10 246	89.75 103	93.15 150

RE-MVS-def			80.48 129		92.02 105	58.56 294	90.90 199	90.45 204	62.76 296	78.89 114	94.46 78	49.30 247		78.77 121	86.77 127	92.28 174

IB-MVS	77.80 4	82.18 112	80.46 130	87.35 38	89.14 177	70.28 31	95.59 25	95.17 15	78.85 63	70.19 214	85.82 220	70.66 36	97.67 55	72.19 168	66.52 274	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
ECVR-MVS		81.29 127	80.38 131	84.01 154	88.39 195	61.96 239	92.56 133	86.79 302	77.66 82	76.63 139	91.42 146	46.34 273	95.24 174	74.36 151	89.23 104	94.85 82

thisisatest0530		81.15 129	80.07 132	84.39 142	88.26 199	65.63 150	91.40 178	94.62 37	71.27 209	70.93 205	89.18 177	72.47 30	96.04 138	65.62 236	76.89 209	91.49 187

1121		81.25 128	80.05 133	84.87 126	92.30 98	64.31 187	87.91 265	91.39 172	59.44 322	79.94 101	92.91 118	57.09 158	97.01 96	66.63 219	92.81 63	93.29 145

test1111		80.84 137	80.02 134	83.33 171	87.87 211	60.76 261	92.62 127	86.86 301	77.86 78	75.73 146	91.39 148	46.35 272	94.70 191	72.79 159	88.68 110	94.52 98

Fast-Effi-MVS+		81.14 130	80.01 135	84.51 139	90.24 150	65.86 143	94.12 61	89.15 257	73.81 137	75.37 153	88.26 191	57.26 156	94.53 200	66.97 217	84.92 140	93.15 150

mvs_anonymous		81.36 126	79.99 136	85.46 105	90.39 148	68.40 69	86.88 280	90.61 202	74.41 122	70.31 213	84.67 232	63.79 90	92.32 277	73.13 154	85.70 135	95.67 46

Vis-MVSNet		80.92 136	79.98 137	83.74 158	88.48 190	61.80 241	93.44 96	88.26 287	73.96 133	77.73 126	91.76 142	49.94 241	94.76 185	65.84 232	90.37 98	94.65 92
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
nrg030		80.93 135	79.86 138	84.13 150	83.69 277	68.83 60	93.23 101	91.20 179	75.55 106	75.06 154	88.22 194	63.04 103	94.74 187	81.88 93	66.88 271	88.82 222

1112_ss		80.56 140	79.83 139	82.77 181	88.65 187	60.78 259	92.29 137	88.36 282	72.58 162	72.46 189	94.95 64	65.09 73	93.42 240	66.38 225	77.71 196	94.10 114

HQP_MVS		80.34 144	79.75 140	82.12 203	86.94 226	62.42 230	93.13 103	91.31 175	78.81 65	72.53 186	89.14 179	50.66 233	95.55 163	76.74 132	78.53 192	88.39 230

UA-Net		80.02 150	79.65 141	81.11 227	89.33 172	57.72 303	86.33 283	89.00 265	77.44 87	81.01 90	89.15 178	59.33 139	95.90 142	61.01 266	84.28 150	89.73 213

Vis-MVSNet (Re-imp)		79.24 165	79.57 142	78.24 280	88.46 191	52.29 337	90.41 214	89.12 259	74.24 126	69.13 224	91.91 140	65.77 67	90.09 312	59.00 278	88.09 116	92.33 171

test-LLR		80.10 148	79.56 143	81.72 212	86.93 228	61.17 251	92.70 122	91.54 165	71.51 204	75.62 148	86.94 208	53.83 201	92.38 273	72.21 166	84.76 143	91.60 185

HyFIR lowres test		81.03 134	79.56 143	85.43 107	87.81 212	68.11 80	90.18 222	90.01 228	70.65 225	72.95 179	86.06 218	63.61 94	94.50 202	75.01 145	79.75 181	93.67 132

HPM-MVS_fast		80.25 145	79.55 145	82.33 193	91.55 124	59.95 275	91.32 186	89.16 256	65.23 280	74.71 160	93.07 114	47.81 262	95.74 148	74.87 149	88.23 113	91.31 195

TAMVS		80.37 143	79.45 146	83.13 176	85.14 254	63.37 210	91.23 189	90.76 196	74.81 120	72.65 183	88.49 183	60.63 122	92.95 247	69.41 192	81.95 162	93.08 153

FIs		79.47 161	79.41 147	79.67 259	85.95 242	59.40 282	91.68 168	93.94 61	78.06 74	68.96 229	88.28 189	66.61 59	91.77 288	66.20 228	74.99 218	87.82 236

IS-MVSNet		80.14 147	79.41 147	82.33 193	87.91 208	60.08 274	91.97 154	88.27 285	72.90 157	71.44 202	91.73 144	61.44 115	93.66 235	62.47 259	86.53 131	93.24 147

test-mter		79.96 151	79.38 149	81.72 212	86.93 228	61.17 251	92.70 122	91.54 165	73.85 135	75.62 148	86.94 208	49.84 243	92.38 273	72.21 166	84.76 143	91.60 185

BH-w/o		80.49 142	79.30 150	84.05 153	90.83 140	64.36 186	93.60 89	89.42 246	74.35 124	69.09 225	90.15 168	55.23 185	95.61 157	64.61 243	86.43 133	92.17 179

EPNet_dtu		78.80 177	79.26 151	77.43 288	88.06 204	49.71 350	91.96 155	91.95 142	77.67 81	76.56 141	91.28 150	58.51 146	90.20 310	56.37 285	80.95 173	92.39 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
abl_6		79.82 154	79.20 152	81.70 214	89.85 158	58.34 296	88.47 258	90.07 223	62.56 299	77.71 127	93.08 112	47.65 264	96.78 113	77.94 127	85.45 138	89.99 210

CPTT-MVS		79.59 158	79.16 153	80.89 236	91.54 125	59.80 277	92.10 144	88.54 280	60.42 314	72.96 178	93.28 109	48.27 256	92.80 256	78.89 120	86.50 132	90.06 208

tpmrst		80.57 139	79.14 154	84.84 127	90.10 152	68.28 74	81.70 310	89.72 239	77.63 84	75.96 144	79.54 304	64.94 76	92.71 259	75.43 139	77.28 206	93.55 135

1314		80.70 138	78.95 155	85.94 87	87.77 213	67.56 93	87.91 265	92.55 121	72.17 177	67.44 250	93.09 111	50.27 237	97.04 94	71.68 174	87.64 119	93.23 148

UGNet		79.87 153	78.68 156	83.45 170	89.96 156	61.51 247	92.13 142	90.79 195	76.83 94	78.85 119	86.33 214	38.16 308	96.17 131	67.93 207	87.17 122	92.67 163
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
PVSNet	73.49 8	80.05 149	78.63 157	84.31 145	90.92 137	64.97 169	92.47 134	91.05 190	79.18 56	72.43 190	90.51 160	37.05 322	94.06 218	68.06 203	86.00 134	93.90 126

Test_1112_low_res		79.56 159	78.60 158	82.43 189	88.24 201	60.39 269	92.09 145	87.99 291	72.10 179	71.84 196	87.42 204	64.62 79	93.04 244	65.80 233	77.30 205	93.85 128

tttt0517		79.50 160	78.53 159	82.41 192	87.22 222	61.43 249	89.75 235	94.76 30	69.29 242	67.91 244	88.06 196	72.92 26	95.63 155	62.91 255	73.90 225	90.16 207

thres200		79.66 156	78.33 160	83.66 164	92.54 94	65.82 146	93.06 105	96.31 3	74.90 119	73.30 176	88.66 181	59.67 134	95.61 157	47.84 317	78.67 190	89.56 216

ab-mvs		80.18 146	78.31 161	85.80 93	88.44 192	65.49 155	83.00 304	92.67 114	71.82 190	77.36 132	85.01 226	54.50 192	96.59 119	76.35 136	75.63 216	95.32 60

VDDNet		80.50 141	78.26 162	87.21 40	86.19 238	69.79 40	94.48 49	91.31 175	60.42 314	79.34 108	90.91 153	38.48 306	96.56 122	82.16 90	81.05 172	95.27 66

EI-MVSNet		78.97 172	78.22 163	81.25 221	85.33 250	62.73 228	89.53 239	93.21 91	72.39 170	72.14 193	90.13 169	60.99 118	94.72 188	67.73 209	72.49 235	86.29 262

OPM-MVS		79.00 171	78.09 164	81.73 211	83.52 280	63.83 198	91.64 170	90.30 214	76.36 100	71.97 195	89.93 172	46.30 275	95.17 175	75.10 142	77.70 197	86.19 266
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
FC-MVSNet-test		77.99 194	78.08 165	77.70 283	84.89 259	55.51 323	90.27 219	93.75 70	76.87 91	66.80 260	87.59 201	65.71 68	90.23 309	62.89 256	73.94 223	87.37 243

VPA-MVSNet		79.03 170	78.00 166	82.11 206	85.95 242	64.48 178	93.22 102	94.66 35	75.05 117	74.04 170	84.95 228	52.17 221	93.52 237	74.90 148	67.04 270	88.32 232

miper_enhance_ethall		78.86 175	77.97 167	81.54 216	88.00 207	65.17 160	91.41 176	89.15 257	75.19 115	68.79 232	83.98 240	67.17 53	92.82 254	72.73 160	65.30 278	86.62 259

mvs-test1		78.74 180	77.95 168	81.14 225	83.22 282	57.13 313	93.96 69	87.78 293	75.42 108	72.68 182	90.80 155	45.08 282	94.54 199	75.08 143	77.49 202	91.74 184

tpm2		79.80 155	77.95 168	85.34 111	88.28 198	68.26 75	81.56 312	91.42 171	70.11 231	77.59 130	80.50 291	67.40 51	94.26 210	67.34 212	77.35 204	93.51 136

OMC-MVS		78.67 183	77.91 170	80.95 234	85.76 246	57.40 311	88.49 257	88.67 275	73.85 135	72.43 190	92.10 137	49.29 248	94.55 198	72.73 160	77.89 195	90.91 200

test_part1		79.63 157	77.86 171	84.93 122	92.50 95	71.43 18	94.15 59	91.08 188	72.51 164	70.66 206	84.98 227	59.84 131	95.07 177	72.07 169	62.94 299	88.30 233

114514_t		79.17 167	77.67 172	83.68 162	95.32 31	65.53 153	92.85 117	91.60 164	63.49 289	67.92 243	90.63 158	46.65 269	95.72 153	67.01 216	83.54 153	89.79 211

BH-RMVSNet		79.46 162	77.65 173	84.89 124	91.68 120	65.66 148	93.55 91	88.09 289	72.93 155	73.37 175	91.12 151	46.20 276	96.12 133	56.28 286	85.61 137	92.91 158

PCF-MVS	73.15 9	79.29 164	77.63 174	84.29 146	86.06 240	65.96 141	87.03 276	91.10 185	69.86 236	69.79 221	90.64 156	57.54 155	96.59 119	64.37 245	82.29 158	90.32 205
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
UniMVSNet_NR-MVSNet		78.15 192	77.55 175	79.98 251	84.46 266	60.26 270	92.25 138	93.20 93	77.50 86	68.88 230	86.61 210	66.10 62	92.13 280	66.38 225	62.55 301	87.54 238

VPNet		78.82 176	77.53 176	82.70 183	84.52 264	66.44 128	93.93 74	92.23 129	80.46 40	72.60 184	88.38 188	49.18 249	93.13 243	72.47 164	63.97 295	88.55 226

GeoE		78.90 174	77.43 177	83.29 172	88.95 181	62.02 237	92.31 136	86.23 308	70.24 230	71.34 203	89.27 176	54.43 196	94.04 221	63.31 251	80.81 175	93.81 129

AUN-MVS		78.37 188	77.43 177	81.17 223	86.60 231	57.45 310	89.46 241	91.16 181	74.11 128	74.40 164	90.49 161	55.52 182	94.57 195	74.73 150	60.43 323	91.48 188

tfpn200view9		78.79 178	77.43 177	82.88 179	92.21 102	64.49 176	92.05 148	96.28 4	73.48 145	71.75 198	88.26 191	60.07 129	95.32 170	45.16 327	77.58 199	88.83 219

thres400		78.68 181	77.43 177	82.43 189	92.21 102	64.49 176	92.05 148	96.28 4	73.48 145	71.75 198	88.26 191	60.07 129	95.32 170	45.16 327	77.58 199	87.48 240

QAPM		79.95 152	77.39 181	87.64 29	89.63 163	71.41 19	93.30 99	93.70 71	65.34 279	67.39 253	91.75 143	47.83 261	98.96 16	57.71 281	89.81 100	92.54 167

TR-MVS		78.77 179	77.37 182	82.95 178	90.49 145	60.88 257	93.67 86	90.07 223	70.08 232	74.51 162	91.37 149	45.69 278	95.70 154	60.12 272	80.32 178	92.29 173

FA-MVS(test-final)		79.12 168	77.23 183	84.81 128	90.54 143	63.98 195	81.35 315	91.71 157	71.09 213	74.85 156	82.94 249	52.85 213	97.05 90	67.97 204	81.73 165	93.41 139

FA-MVS_test		79.34 163	77.11 184	86.03 82	90.07 153	67.55 95	81.35 315	91.90 147	70.96 216	74.72 159	82.94 249	52.85 213	97.05 90	67.97 204	81.22 170	93.50 137

BH-untuned		78.68 181	77.08 185	83.48 169	89.84 159	63.74 201	92.70 122	88.59 278	71.57 201	66.83 259	88.65 182	51.75 224	95.39 168	59.03 277	84.77 142	91.32 194

tpm		78.58 184	77.03 186	83.22 174	85.94 244	64.56 174	83.21 302	91.14 184	78.31 70	73.67 173	79.68 302	64.01 86	92.09 282	66.07 230	71.26 245	93.03 154

thres100view900		78.37 188	77.01 187	82.46 188	91.89 114	63.21 214	91.19 193	96.33 1	72.28 173	70.45 210	87.89 198	60.31 124	95.32 170	45.16 327	77.58 199	88.83 219

AdaColmap		78.94 173	77.00 188	84.76 129	96.34 19	65.86 143	92.66 126	87.97 292	62.18 302	70.56 207	92.37 133	43.53 288	97.35 74	64.50 244	82.86 155	91.05 199

CHOSEN 280x420		77.35 204	76.95 189	78.55 275	87.07 225	62.68 229	69.71 355	82.95 337	68.80 249	71.48 201	87.27 207	66.03 63	84.00 349	76.47 135	82.81 157	88.95 218

FA-MVS_test4		79.23 166	76.85 190	86.35 75	90.54 143	68.30 72	80.92 319	92.46 125	71.09 213	73.41 174	82.30 259	53.25 210	97.01 96	66.20 228	81.51 167	93.74 130

FA-MVS_test2		79.09 169	76.79 191	85.99 84	90.00 155	67.53 96	80.62 321	91.91 146	70.80 222	74.42 163	82.52 255	52.82 215	97.04 94	67.31 214	80.67 177	93.41 139

cl22		77.94 196	76.78 192	81.42 218	87.57 214	64.93 171	90.67 207	88.86 269	72.45 167	67.63 249	82.68 254	64.07 85	92.91 252	71.79 170	65.30 278	86.44 260

UniMVSNet (Re)		77.58 201	76.78 192	79.98 251	84.11 272	60.80 258	91.76 164	93.17 96	76.56 98	69.93 220	84.78 231	63.32 99	92.36 275	64.89 242	62.51 303	86.78 254

thres600view7		78.00 193	76.66 194	82.03 208	91.93 111	63.69 205	91.30 187	96.33 1	72.43 168	70.46 209	87.89 198	60.31 124	94.92 183	42.64 339	76.64 210	87.48 240

MS-PatchMatch		77.90 198	76.50 195	82.12 203	85.99 241	69.95 36	91.75 166	92.70 112	73.97 132	62.58 294	84.44 235	41.11 295	95.78 145	63.76 249	92.17 72	80.62 337

miper_ehance_all_eth		77.60 200	76.44 196	81.09 231	85.70 247	64.41 183	90.65 208	88.64 277	72.31 171	67.37 254	82.52 255	64.77 78	92.64 266	70.67 180	65.30 278	86.24 264

XXY-MVS		77.94 196	76.44 196	82.43 189	82.60 289	64.44 180	92.01 150	91.83 152	73.59 143	70.00 217	85.82 220	54.43 196	94.76 185	69.63 189	68.02 265	88.10 235

PS-MVSNAJss		77.26 205	76.31 198	80.13 248	80.64 305	59.16 287	90.63 211	91.06 189	72.80 158	68.58 236	84.57 234	53.55 205	93.96 226	72.97 155	71.96 239	87.27 248

FA-MVS_test3		78.53 186	76.27 199	85.30 112	89.34 169	66.26 134	80.03 330	91.71 157	69.92 235	74.58 161	82.47 258	50.02 239	96.78 113	66.64 218	81.73 165	92.87 161

MVP-Stereo		77.12 208	76.23 200	79.79 257	81.72 296	66.34 131	89.29 243	90.88 194	70.56 227	62.01 297	82.88 251	49.34 246	94.13 213	65.55 238	93.80 45	78.88 350
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GA-MVS		78.33 190	76.23 200	84.65 134	83.65 278	66.30 132	91.44 174	90.14 221	76.01 102	70.32 212	84.02 239	42.50 291	94.72 188	70.98 176	77.00 208	92.94 157

FMVSNet3		77.73 199	76.04 202	82.80 180	91.20 133	68.99 57	91.87 157	91.99 140	73.35 147	67.04 256	83.19 248	56.62 170	92.14 279	59.80 274	69.34 254	87.28 247

EPMVS		78.49 187	75.98 203	86.02 83	91.21 132	69.68 44	80.23 326	91.20 179	75.25 114	72.48 188	78.11 312	54.65 191	93.69 234	57.66 282	83.04 154	94.69 88

OpenMVS	70.45 11	78.54 185	75.92 204	86.41 71	85.93 245	71.68 17	92.74 119	92.51 122	66.49 270	64.56 275	91.96 139	43.88 287	98.10 39	54.61 290	90.65 95	89.44 217

DU-MVS		76.86 209	75.84 205	79.91 253	82.96 287	60.26 270	91.26 188	91.54 165	76.46 99	68.88 230	86.35 212	56.16 174	92.13 280	66.38 225	62.55 301	87.35 245

cascas		78.18 191	75.77 206	85.41 108	87.14 224	69.11 52	92.96 111	91.15 183	66.71 268	70.47 208	86.07 217	37.49 316	96.48 126	70.15 184	79.80 180	90.65 202

WR-MVS		76.76 214	75.74 207	79.82 256	84.60 262	62.27 235	92.60 128	92.51 122	76.06 101	67.87 246	85.34 223	56.76 166	90.24 308	62.20 260	63.69 297	86.94 252

mvsmamba		76.85 211	75.71 208	80.25 245	83.07 286	59.16 287	91.44 174	80.64 346	76.84 93	67.95 242	86.33 214	46.17 277	94.24 211	76.06 137	72.92 231	87.36 244

v2v482		77.42 203	75.65 209	82.73 182	80.38 307	67.13 110	91.85 159	90.23 218	75.09 116	69.37 222	83.39 246	53.79 203	94.44 203	71.77 171	65.00 284	86.63 258

c3_l		76.83 213	75.47 210	80.93 235	85.02 257	64.18 192	90.39 215	88.11 288	71.66 194	66.65 261	81.64 271	63.58 96	92.56 267	69.31 194	62.86 300	86.04 271

Anonymous202405211		77.96 195	75.33 211	85.87 89	93.73 62	64.52 175	94.85 45	85.36 316	62.52 300	76.11 143	90.18 167	29.43 348	97.29 78	68.51 201	77.24 207	95.81 45

Effi-MVS+-dtu		76.14 219	75.28 212	78.72 274	83.22 282	55.17 325	89.87 231	87.78 293	75.42 108	67.98 241	81.43 275	45.08 282	92.52 269	75.08 143	71.63 240	88.48 227

IterMVS-LS		76.49 216	75.18 213	80.43 240	84.49 265	62.74 227	90.64 209	88.80 270	72.40 169	65.16 269	81.72 269	60.98 119	92.27 278	67.74 208	64.65 289	86.29 262
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FA-MVS(test)		77.26 205	74.98 214	84.09 151	87.88 210	63.98 195	78.42 337	90.50 203	67.69 258	74.85 156	82.30 259	46.75 267	96.50 125	65.71 234	80.76 176	91.79 183

v1144		76.73 215	74.88 215	82.27 195	80.23 312	66.60 125	91.68 168	90.21 220	73.69 140	69.06 227	81.89 266	52.73 217	94.40 204	69.21 195	65.23 281	85.80 277

cl____		76.07 220	74.67 216	80.28 243	85.15 253	61.76 243	90.12 223	88.73 273	71.16 210	65.43 266	81.57 273	61.15 116	92.95 247	66.54 222	62.17 305	86.13 269

DIV-MVS_self_test		76.07 220	74.67 216	80.28 243	85.14 254	61.75 244	90.12 223	88.73 273	71.16 210	65.42 267	81.60 272	61.15 116	92.94 251	66.54 222	62.16 307	86.14 267

PatchmatchNet		77.46 202	74.63 218	85.96 86	89.55 166	70.35 30	79.97 331	89.55 242	72.23 174	70.94 204	76.91 323	57.03 160	92.79 257	54.27 292	81.17 171	94.74 87
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
NR-MVSNet		76.05 223	74.59 219	80.44 239	82.96 287	62.18 236	90.83 203	91.73 155	77.12 90	60.96 301	86.35 212	59.28 140	91.80 287	60.74 267	61.34 316	87.35 245

LPG-MVS_test		75.82 228	74.58 220	79.56 263	84.31 269	59.37 283	90.44 212	89.73 237	69.49 239	64.86 270	88.42 184	38.65 304	94.30 206	72.56 162	72.76 232	85.01 291

V42		76.46 217	74.55 221	82.19 199	79.14 324	67.82 87	90.26 220	89.42 246	73.75 138	68.63 235	81.89 266	51.31 228	94.09 215	71.69 173	64.84 285	84.66 294

TranMVSNet+NR-MVSNet		75.86 227	74.52 222	79.89 254	82.44 290	60.64 266	91.37 183	91.37 173	76.63 96	67.65 248	86.21 216	52.37 220	91.55 292	61.84 262	60.81 319	87.48 240

v148		76.19 218	74.47 223	81.36 219	80.05 313	64.44 180	91.75 166	90.23 218	73.68 141	67.13 255	80.84 286	55.92 180	93.86 232	68.95 198	61.73 312	85.76 280

eth_miper_zixun_eth		75.96 226	74.40 224	80.66 237	84.66 261	63.02 218	89.28 244	88.27 285	71.88 185	65.73 264	81.65 270	59.45 136	92.81 255	68.13 202	60.53 321	86.14 267

gg-mvs-nofinetune		77.18 207	74.31 225	85.80 93	91.42 127	68.36 70	71.78 349	94.72 32	49.61 353	77.12 135	45.92 370	77.41 8	93.98 225	67.62 210	93.16 57	95.05 76

CVMVSNet		74.04 247	74.27 226	73.33 320	85.33 250	43.94 366	89.53 239	88.39 281	54.33 342	70.37 211	90.13 169	49.17 250	84.05 347	61.83 263	79.36 183	91.99 180

ACMP	71.68 10	75.58 233	74.23 227	79.62 261	84.97 258	59.64 278	90.80 204	89.07 262	70.39 228	62.95 290	87.30 206	38.28 307	93.87 230	72.89 156	71.45 243	85.36 286
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20240529		76.84 212	74.15 228	84.88 125	91.02 134	64.95 170	93.84 82	91.09 186	53.57 343	73.00 177	87.42 204	35.91 326	97.32 76	69.14 196	72.41 237	92.36 170

X-MVStestdata		76.86 209	74.13 229	85.05 118	93.22 73	63.78 199	92.92 115	92.66 115	73.99 130	78.18 123	10.19 382	55.25 183	97.41 70	79.16 115	91.58 82	93.95 122

v144192		76.05 223	74.03 230	82.12 203	79.50 318	66.55 127	91.39 180	89.71 240	72.30 172	68.17 239	81.33 278	51.75 224	94.03 223	67.94 206	64.19 291	85.77 278

FMVSNet2		76.07 220	74.01 231	82.26 197	88.85 182	67.66 91	91.33 185	91.61 163	70.84 219	65.98 263	82.25 262	48.03 257	92.00 284	58.46 279	68.73 260	87.10 249

v1192		75.98 225	73.92 232	82.15 200	79.73 314	66.24 135	91.22 190	89.75 234	72.67 160	68.49 237	81.42 276	49.86 242	94.27 208	67.08 215	65.02 283	85.95 274

GBi-Net		75.65 230	73.83 233	81.10 228	88.85 182	65.11 163	90.01 227	90.32 210	70.84 219	67.04 256	80.25 296	48.03 257	91.54 293	59.80 274	69.34 254	86.64 255

test1		75.65 230	73.83 233	81.10 228	88.85 182	65.11 163	90.01 227	90.32 210	70.84 219	67.04 256	80.25 296	48.03 257	91.54 293	59.80 274	69.34 254	86.64 255

PLC	68.80 14	75.23 236	73.68 235	79.86 255	92.93 82	58.68 293	90.64 209	88.30 283	60.90 311	64.43 279	90.53 159	42.38 292	94.57 195	56.52 284	76.54 211	86.33 261
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAPA-MVS	70.22 12	74.94 240	73.53 236	79.17 268	90.40 147	52.07 338	89.19 247	89.61 241	62.69 298	70.07 215	92.67 125	48.89 254	94.32 205	38.26 353	79.97 179	91.12 198
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1921920		75.63 232	73.49 237	82.06 207	79.38 319	66.35 130	91.07 197	89.48 243	71.98 180	67.99 240	81.22 281	49.16 251	93.90 229	66.56 221	64.56 290	85.92 276

Fast-Effi-MVS+-dtu		75.04 238	73.37 238	80.07 249	80.86 301	59.52 281	91.20 192	85.38 315	71.90 183	65.20 268	84.84 230	41.46 294	92.97 246	66.50 224	72.96 230	87.73 237

v8		75.35 234	73.26 239	81.61 215	80.67 304	66.82 118	89.54 238	89.27 250	71.65 195	63.30 288	80.30 295	54.99 189	94.06 218	67.33 213	62.33 304	83.94 299

XVG-OURS-SEG-HR		74.70 242	73.08 240	79.57 262	78.25 334	57.33 312	80.49 322	87.32 297	63.22 292	68.76 233	90.12 171	44.89 284	91.59 291	70.55 182	74.09 222	89.79 211

v1240		75.21 237	72.98 241	81.88 209	79.20 321	66.00 139	90.75 206	89.11 260	71.63 199	67.41 252	81.22 281	47.36 265	93.87 230	65.46 239	64.72 288	85.77 278

RRT_MVS		74.44 243	72.97 242	78.84 273	82.36 291	57.66 305	89.83 233	88.79 272	70.61 226	64.58 274	84.89 229	39.24 300	92.65 265	70.11 185	66.34 275	86.21 265

Baseline_NR-MVSNet		73.99 248	72.83 243	77.48 287	80.78 302	59.29 286	91.79 161	84.55 324	68.85 248	68.99 228	80.70 287	56.16 174	92.04 283	62.67 257	60.98 318	81.11 331

SCA		75.82 228	72.76 244	85.01 120	86.63 230	70.08 32	81.06 318	89.19 254	71.60 200	70.01 216	77.09 321	45.53 279	90.25 305	60.43 269	73.27 227	94.68 89

ACMM	69.62 13	74.34 244	72.73 245	79.17 268	84.25 271	57.87 301	90.36 216	89.93 229	63.17 293	65.64 265	86.04 219	37.79 314	94.10 214	65.89 231	71.52 242	85.55 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test0.0.03 1		72.76 260	72.71 246	72.88 324	80.25 311	47.99 356	91.22 190	89.45 244	71.51 204	62.51 295	87.66 200	53.83 201	85.06 344	50.16 304	67.84 268	85.58 281

MDTV_nov1_ep13			72.61 247		89.06 178	68.48 67	80.33 324	90.11 222	71.84 189	71.81 197	75.92 331	53.01 212	93.92 228	48.04 314	73.38 226

test_djsdf		73.76 252	72.56 248	77.39 289	77.00 343	53.93 330	89.07 249	90.69 197	65.80 274	63.92 281	82.03 265	43.14 290	92.67 262	72.83 157	68.53 261	85.57 282

v10		74.77 241	72.54 249	81.46 217	80.33 310	66.71 122	89.15 248	89.08 261	70.94 217	63.08 289	79.86 300	52.52 218	94.04 221	65.70 235	62.17 305	83.64 301

XVG-OURS		74.25 246	72.46 250	79.63 260	78.45 333	57.59 307	80.33 324	87.39 296	63.86 287	68.76 233	89.62 175	40.50 297	91.72 289	69.00 197	74.25 220	89.58 214

CNLPA		74.31 245	72.30 251	80.32 241	91.49 126	61.66 245	90.85 202	80.72 345	56.67 336	63.85 283	90.64 156	46.75 267	90.84 300	53.79 294	75.99 215	88.47 229

tpm cat1		75.30 235	72.21 252	84.58 137	88.52 188	67.77 88	78.16 341	88.02 290	61.88 307	68.45 238	76.37 327	60.65 121	94.03 223	53.77 295	74.11 221	91.93 181

dp		75.01 239	72.09 253	83.76 157	89.28 173	66.22 136	79.96 332	89.75 234	71.16 210	67.80 247	77.19 320	51.81 223	92.54 268	50.39 303	71.44 244	92.51 168

D2MVS		73.80 250	72.02 254	79.15 270	79.15 323	62.97 219	88.58 256	90.07 223	72.94 154	59.22 309	78.30 309	42.31 293	92.70 261	65.59 237	72.00 238	81.79 328

LCM-MVSNet-Re		72.93 257	71.84 255	76.18 303	88.49 189	48.02 355	80.07 329	70.17 367	73.96 133	52.25 337	80.09 299	49.98 240	88.24 325	67.35 211	84.23 151	92.28 174

pmmvs4		73.92 249	71.81 256	80.25 245	79.17 322	65.24 158	87.43 273	87.26 299	67.64 262	63.46 286	83.91 241	48.96 253	91.53 296	62.94 254	65.49 277	83.96 298

miper_lstm_enhance		73.05 255	71.73 257	77.03 294	83.80 275	58.32 297	81.76 308	88.88 267	69.80 237	61.01 300	78.23 311	57.19 157	87.51 334	65.34 240	59.53 326	85.27 289

pmmvs5		73.35 253	71.52 258	78.86 272	78.64 331	60.61 267	91.08 195	86.90 300	67.69 258	63.32 287	83.64 242	44.33 286	90.53 302	62.04 261	66.02 276	85.46 284

jajsoiax		73.05 255	71.51 259	77.67 284	77.46 340	54.83 326	88.81 252	90.04 227	69.13 246	62.85 292	83.51 244	31.16 343	92.75 258	70.83 177	69.80 250	85.43 285

mvs_tets		72.71 262	71.11 260	77.52 285	77.41 341	54.52 328	88.45 259	89.76 233	68.76 251	62.70 293	83.26 247	29.49 347	92.71 259	70.51 183	69.62 252	85.34 287

pm-mvs1		72.89 258	71.09 261	78.26 279	79.10 325	57.62 306	90.80 204	89.30 249	67.66 260	62.91 291	81.78 268	49.11 252	92.95 247	60.29 271	58.89 329	84.22 297

IterMVS		72.65 264	70.83 262	78.09 281	82.17 292	62.96 220	87.64 271	86.28 306	71.56 202	60.44 303	78.85 307	45.42 281	86.66 338	63.30 252	61.83 309	84.65 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CR-MVSNet		73.79 251	70.82 263	82.70 183	83.15 284	67.96 83	70.25 352	84.00 329	73.67 142	69.97 218	72.41 342	57.82 152	89.48 316	52.99 298	73.13 228	90.64 203

UniMVSNet_ETH3D		72.74 261	70.53 264	79.36 265	78.62 332	56.64 316	85.01 287	89.20 253	63.77 288	64.84 272	84.44 235	34.05 331	91.86 286	63.94 247	70.89 247	89.57 215

Anonymous20231211		73.08 254	70.39 265	81.13 226	90.62 142	63.33 211	91.40 178	90.06 226	51.84 347	64.46 278	80.67 289	36.49 324	94.07 217	63.83 248	64.17 292	85.98 273

PatchMatch-RL		72.06 265	69.98 266	78.28 278	89.51 167	55.70 322	83.49 296	83.39 335	61.24 310	63.72 284	82.76 252	34.77 329	93.03 245	53.37 297	77.59 198	86.12 270

IterMVS-SCA-FT		71.55 269	69.97 267	76.32 301	81.48 297	60.67 265	87.64 271	85.99 311	66.17 272	59.50 307	78.88 306	45.53 279	83.65 351	62.58 258	61.93 308	84.63 296

WR-MVS_H		70.59 273	69.94 268	72.53 326	81.03 300	51.43 341	87.35 274	92.03 139	67.38 263	60.23 304	80.70 287	55.84 181	83.45 353	46.33 323	58.58 331	82.72 316

CP-MVSNet		70.50 274	69.91 269	72.26 329	80.71 303	51.00 344	87.23 275	90.30 214	67.84 256	59.64 306	82.69 253	50.23 238	82.30 361	51.28 300	59.28 327	83.46 306

FMVSNet1		72.71 262	69.91 269	81.10 228	83.60 279	65.11 163	90.01 227	90.32 210	63.92 286	63.56 285	80.25 296	36.35 325	91.54 293	54.46 291	66.75 272	86.64 255

tpmvs		72.88 259	69.76 271	82.22 198	90.98 135	67.05 112	78.22 340	88.30 283	63.10 294	64.35 280	74.98 334	55.09 188	94.27 208	43.25 333	69.57 253	85.34 287

bld_raw_dy_0_64		71.59 268	69.71 272	77.22 293	77.82 339	58.12 299	87.71 269	73.66 360	68.01 255	61.90 299	84.29 237	33.68 332	88.43 323	69.91 187	70.43 248	85.11 290

anonymousdsp		71.14 271	69.37 273	76.45 300	72.95 355	54.71 327	84.19 291	88.88 267	61.92 306	62.15 296	79.77 301	38.14 309	91.44 298	68.90 199	67.45 269	83.21 310

PS-CasMVS		69.86 280	69.13 274	72.07 332	80.35 309	50.57 346	87.02 277	89.75 234	67.27 264	59.19 310	82.28 261	46.58 270	82.24 362	50.69 302	59.02 328	83.39 308

v7n		71.31 270	68.65 275	79.28 266	76.40 345	60.77 260	86.71 281	89.45 244	64.17 285	58.77 314	78.24 310	44.59 285	93.54 236	57.76 280	61.75 311	83.52 304

PEN-MVS		69.46 282	68.56 276	72.17 331	79.27 320	49.71 350	86.90 279	89.24 251	67.24 267	59.08 311	82.51 257	47.23 266	83.54 352	48.42 312	57.12 332	83.25 309

MIMVSNet		71.64 267	68.44 277	81.23 222	81.97 295	64.44 180	73.05 348	88.80 270	69.67 238	64.59 273	74.79 335	32.79 335	87.82 329	53.99 293	76.35 213	91.42 189

F-COLMAP		70.66 272	68.44 277	77.32 290	86.37 236	55.91 320	88.00 263	86.32 305	56.94 334	57.28 323	88.07 195	33.58 333	92.49 270	51.02 301	68.37 262	83.55 302

PVSNet_0	68.08 15	71.81 266	68.32 279	82.27 195	84.68 260	62.31 234	88.68 254	90.31 213	75.84 103	57.93 320	80.65 290	37.85 313	94.19 212	69.94 186	29.05 374	90.31 206

CL-MVSNet_self_test		69.92 278	68.09 280	75.41 306	73.25 354	55.90 321	90.05 226	89.90 230	69.96 233	61.96 298	76.54 324	51.05 231	87.64 331	49.51 308	50.59 350	82.70 318

TransMVSNet (Re)		70.07 277	67.66 281	77.31 291	80.62 306	59.13 289	91.78 163	84.94 320	65.97 273	60.08 305	80.44 292	50.78 232	91.87 285	48.84 310	45.46 358	80.94 333

tfpnnormal		70.10 276	67.36 282	78.32 277	83.45 281	60.97 256	88.85 251	92.77 110	64.85 281	60.83 302	78.53 308	43.52 289	93.48 238	31.73 369	61.70 313	80.52 338

DTE-MVSNet		68.46 291	67.33 283	71.87 334	77.94 337	49.00 353	86.16 284	88.58 279	66.36 271	58.19 315	82.21 263	46.36 271	83.87 350	44.97 330	55.17 339	82.73 315

MVS_0304		68.99 287	67.23 284	74.28 315	80.36 308	52.54 335	87.01 278	86.36 304	59.89 320	66.22 262	73.56 338	24.25 357	88.03 327	57.34 283	70.11 249	82.27 324

DP-MVS		69.90 279	66.48 285	80.14 247	95.36 30	62.93 221	89.56 236	76.11 353	50.27 352	57.69 321	85.23 224	39.68 299	95.73 149	33.35 363	71.05 246	81.78 329

LS3D		69.17 283	66.40 286	77.50 286	91.92 112	56.12 319	85.12 286	80.37 347	46.96 359	56.50 325	87.51 203	37.25 317	93.71 233	32.52 368	79.40 182	82.68 319

KD-MVS_2432*1600		69.03 285	66.37 287	77.01 295	85.56 248	61.06 254	81.44 313	90.25 216	67.27 264	58.00 318	76.53 325	54.49 193	87.63 332	48.04 314	35.77 369	82.34 322

miper_refine_blended		69.03 285	66.37 287	77.01 295	85.56 248	61.06 254	81.44 313	90.25 216	67.27 264	58.00 318	76.53 325	54.49 193	87.63 332	48.04 314	35.77 369	82.34 322

Anonymous20231206		67.53 299	65.78 289	72.79 325	74.95 349	47.59 358	88.23 261	87.32 297	61.75 309	58.07 317	77.29 318	37.79 314	87.29 336	42.91 335	63.71 296	83.48 305

MSDG		69.54 281	65.73 290	80.96 233	85.11 256	63.71 203	84.19 291	83.28 336	56.95 333	54.50 328	84.03 238	31.50 341	96.03 139	42.87 337	69.13 257	83.14 312

RPMNet		70.42 275	65.68 291	84.63 136	83.15 284	67.96 83	70.25 352	90.45 204	46.83 361	69.97 218	65.10 359	56.48 173	95.30 173	35.79 358	73.13 228	90.64 203

FMVSNet5		68.04 294	65.66 292	75.18 308	84.43 267	57.89 300	83.54 295	86.26 307	61.83 308	53.64 333	73.30 339	37.15 320	85.08 343	48.99 309	61.77 310	82.56 321

XVG-ACMP-BASELINE		68.04 294	65.53 293	75.56 305	74.06 353	52.37 336	78.43 336	85.88 312	62.03 304	58.91 313	81.21 283	20.38 366	91.15 299	60.69 268	68.18 263	83.16 311

EG-PatchMatch MVS		68.55 289	65.41 294	77.96 282	78.69 330	62.93 221	89.86 232	89.17 255	60.55 313	50.27 345	77.73 315	22.60 362	94.06 218	47.18 320	72.65 234	76.88 357

PatchT		69.11 284	65.37 295	80.32 241	82.07 294	63.68 206	67.96 361	87.62 295	50.86 350	69.37 222	65.18 358	57.09 158	88.53 322	41.59 342	66.60 273	88.74 223

ACMH	63.93 17	68.62 288	64.81 296	80.03 250	85.22 252	63.25 212	87.72 268	84.66 323	60.83 312	51.57 340	79.43 305	27.29 353	94.96 180	41.76 340	64.84 285	81.88 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs6		67.57 298	64.76 297	76.00 304	72.82 357	53.37 332	88.71 253	86.78 303	53.19 344	57.58 322	78.03 313	35.33 328	92.41 272	55.56 288	54.88 341	82.21 325

our_test_3		68.29 292	64.69 298	79.11 271	78.92 326	64.85 172	88.40 260	85.06 318	60.32 316	52.68 335	76.12 329	40.81 296	89.80 315	44.25 332	55.65 337	82.67 320

ACMH+	65.35 16	67.65 297	64.55 299	76.96 297	84.59 263	57.10 314	88.08 262	80.79 344	58.59 327	53.00 334	81.09 285	26.63 355	92.95 247	46.51 321	61.69 314	80.82 334

USDC		67.43 301	64.51 300	76.19 302	77.94 337	55.29 324	78.38 338	85.00 319	73.17 149	48.36 351	80.37 293	21.23 364	92.48 271	52.15 299	64.02 294	80.81 335

Patchmatch-RL test		68.17 293	64.49 301	79.19 267	71.22 359	53.93 330	70.07 354	71.54 366	69.22 243	56.79 324	62.89 361	56.58 171	88.61 319	69.53 191	52.61 345	95.03 79

CMPMVS	48.56 21	66.77 303	64.41 302	73.84 317	70.65 362	50.31 347	77.79 342	85.73 314	45.54 362	44.76 360	82.14 264	35.40 327	90.14 311	63.18 253	74.54 219	81.07 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		68.54 290	64.38 303	81.03 232	88.06 204	66.90 117	68.01 359	84.02 328	57.57 328	64.48 276	69.87 351	38.68 302	89.21 318	40.87 344	67.89 266	86.97 250

Patchmtry		67.53 299	63.93 304	78.34 276	82.12 293	64.38 184	68.72 356	84.00 329	48.23 358	59.24 308	72.41 342	57.82 152	89.27 317	46.10 324	56.68 336	81.36 330

ppachtmachnet_test		67.72 296	63.70 305	79.77 258	78.92 326	66.04 138	88.68 254	82.90 338	60.11 318	55.45 326	75.96 330	39.19 301	90.55 301	39.53 348	52.55 346	82.71 317

LTVRE_ROB	59.60 19	66.27 305	63.54 306	74.45 312	84.00 274	51.55 340	67.08 362	83.53 332	58.78 325	54.94 327	80.31 294	34.54 330	93.23 242	40.64 346	68.03 264	78.58 353
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
ADS-MVSNet2		66.90 302	63.44 307	77.26 292	88.06 204	60.70 264	68.01 359	75.56 356	57.57 328	64.48 276	69.87 351	38.68 302	84.10 346	40.87 344	67.89 266	86.97 250

UnsupCasMVSNet_eth		65.79 308	63.10 308	73.88 316	70.71 361	50.29 348	81.09 317	89.88 231	72.58 162	49.25 349	74.77 336	32.57 337	87.43 335	55.96 287	41.04 364	83.90 300

EU-MVSNet		64.01 315	63.01 309	67.02 346	74.40 352	38.86 375	83.27 300	86.19 309	45.11 363	54.27 329	81.15 284	36.91 323	80.01 366	48.79 311	57.02 333	82.19 326

OpenMVS_ROB	61.12 18	66.39 304	62.92 310	76.80 299	76.51 344	57.77 302	89.22 245	83.41 334	55.48 340	53.86 332	77.84 314	26.28 356	93.95 227	34.90 360	68.76 259	78.68 352

testgi		64.48 313	62.87 311	69.31 339	71.24 358	40.62 370	85.49 285	79.92 348	65.36 278	54.18 330	83.49 245	23.74 360	84.55 345	41.60 341	60.79 320	82.77 314

test20.03		63.83 316	62.65 312	67.38 345	70.58 363	39.94 371	86.57 282	84.17 326	63.29 291	51.86 338	77.30 317	37.09 321	82.47 359	38.87 352	54.13 343	79.73 344

JIA-IIPM		66.06 306	62.45 313	76.88 298	81.42 299	54.45 329	57.49 370	88.67 275	49.36 354	63.86 282	46.86 369	56.06 177	90.25 305	49.53 307	68.83 258	85.95 274

pmmvs-eth3d		65.53 309	62.32 314	75.19 307	69.39 365	59.59 279	82.80 305	83.43 333	62.52 300	51.30 342	72.49 340	32.86 334	87.16 337	55.32 289	50.73 349	78.83 351

OurMVSNet-221017-0		64.68 311	62.17 315	72.21 330	76.08 348	47.35 359	80.67 320	81.02 343	56.19 337	51.60 339	79.66 303	27.05 354	88.56 321	53.60 296	53.63 344	80.71 336

RPSCF		64.24 314	61.98 316	71.01 336	76.10 347	45.00 363	75.83 345	75.94 354	46.94 360	58.96 312	84.59 233	31.40 342	82.00 363	47.76 318	60.33 325	86.04 271

SixPastTwentyTwo		64.92 310	61.78 317	74.34 314	78.74 329	49.76 349	83.42 299	79.51 350	62.86 295	50.27 345	77.35 316	30.92 345	90.49 303	45.89 325	47.06 355	82.78 313

test_0402		64.54 312	61.09 318	74.92 309	84.10 273	60.75 262	87.95 264	79.71 349	52.03 346	52.41 336	77.20 319	32.21 339	91.64 290	23.14 372	61.03 317	72.36 364

Patchmatch-test		65.86 307	60.94 319	80.62 238	83.75 276	58.83 291	58.91 369	75.26 358	44.50 365	50.95 344	77.09 321	58.81 145	87.90 328	35.13 359	64.03 293	95.12 73

MDA-MVSNet_test_wron		63.78 317	60.16 320	74.64 310	78.15 335	60.41 268	83.49 296	84.03 327	56.17 339	39.17 368	71.59 348	37.22 318	83.24 356	42.87 337	48.73 352	80.26 341

YYNet1		63.76 318	60.14 321	74.62 311	78.06 336	60.19 273	83.46 298	83.99 331	56.18 338	39.25 367	71.56 349	37.18 319	83.34 354	42.90 336	48.70 353	80.32 340

COLMAP_ROB	57.96 20	62.98 320	59.65 322	72.98 323	81.44 298	53.00 334	83.75 294	75.53 357	48.34 357	48.81 350	81.40 277	24.14 358	90.30 304	32.95 365	60.52 322	75.65 360
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
K. test v3		63.09 319	59.61 323	73.53 319	76.26 346	49.38 352	83.27 300	77.15 352	64.35 283	47.77 353	72.32 344	28.73 349	87.79 330	49.93 306	36.69 368	83.41 307

Anonymous20240521		62.09 321	59.08 324	71.10 335	67.19 367	48.72 354	83.91 293	85.23 317	50.38 351	47.84 352	71.22 350	20.74 365	85.51 342	46.47 322	58.75 330	79.06 349

KD-MVS_self_test		60.87 325	58.60 325	67.68 343	66.13 368	39.93 372	75.63 346	84.70 322	57.32 331	49.57 348	68.45 354	29.55 346	82.87 357	48.09 313	47.94 354	80.25 342

AllTest		61.66 322	58.06 326	72.46 327	79.57 315	51.42 342	80.17 327	68.61 369	51.25 348	45.88 355	81.23 279	19.86 367	86.58 339	38.98 350	57.01 334	79.39 346

UnsupCasMVSNet_bld		61.60 323	57.71 327	73.29 321	68.73 366	51.64 339	78.61 335	89.05 263	57.20 332	46.11 354	61.96 363	28.70 350	88.60 320	50.08 305	38.90 366	79.63 345

MDA-MVSNet-bldmvs		61.54 324	57.70 328	73.05 322	79.53 317	57.00 315	83.08 303	81.23 342	57.57 328	34.91 370	72.45 341	32.79 335	86.26 341	35.81 357	41.95 362	75.89 359

MIMVSNet1		60.16 328	57.33 329	68.67 340	69.71 364	44.13 365	78.92 334	84.21 325	55.05 341	44.63 361	71.85 346	23.91 359	81.54 365	32.63 367	55.03 340	80.35 339

PM-MVS		59.40 329	56.59 330	67.84 341	63.63 370	41.86 367	76.76 343	63.22 375	59.01 324	51.07 343	72.27 345	11.72 375	83.25 355	61.34 264	50.28 351	78.39 354

new-patchmatchnet		59.30 330	56.48 331	67.79 342	65.86 369	44.19 364	82.47 306	81.77 340	59.94 319	43.65 364	66.20 357	27.67 352	81.68 364	39.34 349	41.40 363	77.50 356

TinyColmap		60.32 326	56.42 332	72.00 333	78.78 328	53.18 333	78.36 339	75.64 355	52.30 345	41.59 366	75.82 332	14.76 372	88.35 324	35.84 356	54.71 342	74.46 361

MVS-HIRNet		60.25 327	55.55 333	74.35 313	84.37 268	56.57 317	71.64 350	74.11 359	34.44 370	45.54 359	42.24 373	31.11 344	89.81 313	40.36 347	76.10 214	76.67 358

DSMNet-mixed		56.78 331	54.44 334	63.79 348	63.21 371	29.44 379	64.43 364	64.10 374	42.12 367	51.32 341	71.60 347	31.76 340	75.04 368	36.23 355	65.20 282	86.87 253

LF4IMVS		54.01 334	52.12 335	59.69 349	62.41 373	39.91 373	68.59 357	68.28 371	42.96 366	44.55 362	75.18 333	14.09 374	68.39 372	41.36 343	51.68 347	70.78 365

TDRefinement		55.28 333	51.58 336	66.39 347	59.53 375	46.15 362	76.23 344	72.80 362	44.60 364	42.49 365	76.28 328	15.29 370	82.39 360	33.20 364	43.75 360	70.62 366

pmmvs3		55.51 332	51.50 337	67.53 344	57.90 376	50.93 345	80.37 323	73.66 360	40.63 368	44.15 363	64.75 360	16.30 369	78.97 367	44.77 331	40.98 365	72.69 362

N_pmnet		50.55 335	49.11 338	54.88 353	77.17 342	4.02 389	84.36 290	2.00 388	48.59 355	45.86 357	68.82 353	32.22 338	82.80 358	31.58 370	51.38 348	77.81 355

new_pmnet		49.31 336	46.44 339	57.93 350	62.84 372	40.74 369	68.47 358	62.96 376	36.48 369	35.09 369	57.81 365	14.97 371	72.18 370	32.86 366	46.44 356	60.88 370

FPMVS		45.64 337	43.10 340	53.23 355	51.42 379	36.46 376	64.97 363	71.91 364	29.13 372	27.53 372	61.55 364	9.83 377	65.01 376	16.00 376	55.58 338	58.22 371

EGC-MVSNET		42.35 338	38.09 341	55.11 352	74.57 350	46.62 361	71.63 351	55.77 378	0.04 383	0.24 384	62.70 362	14.24 373	74.91 369	17.59 375	46.06 357	43.80 372

LCM-MVSNet		40.54 339	35.79 342	54.76 354	36.92 384	30.81 378	51.41 371	69.02 368	22.07 374	24.63 373	45.37 371	4.56 384	65.81 374	33.67 362	34.50 371	67.67 367

ANet_high		40.27 340	35.20 343	55.47 351	34.74 385	34.47 377	63.84 365	71.56 365	48.42 356	18.80 376	41.08 374	9.52 378	64.45 377	20.18 373	8.66 381	67.49 368

test_method		38.59 341	35.16 344	48.89 357	54.33 377	21.35 384	45.32 374	53.71 379	7.41 380	28.74 371	51.62 367	8.70 379	52.87 378	33.73 361	32.89 372	72.47 363

PMMVS2		37.93 342	33.61 345	50.92 356	46.31 381	24.76 382	60.55 368	50.05 380	28.94 373	20.93 374	47.59 368	4.41 385	65.13 375	25.14 371	18.55 376	62.87 369

Gipuma		34.91 343	31.44 346	45.30 358	70.99 360	39.64 374	19.85 378	72.56 363	20.10 376	16.16 378	21.47 379	5.08 383	71.16 371	13.07 377	43.70 361	25.08 376
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	26.43 22	31.84 344	28.16 347	42.89 359	25.87 387	27.58 380	50.92 372	49.78 381	21.37 375	14.17 379	40.81 375	2.01 386	66.62 373	9.61 379	38.88 367	34.49 375
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
cdsmvs_eth3d_5k		19.86 349	26.47 348	0.00 368	0.00 391	0.00 392	0.00 379	93.45 82	0.00 386	0.00 387	95.27 52	49.56 244	0.00 387	0.00 385	0.00 384	0.00 383

E-PMN		24.61 345	24.00 349	26.45 362	43.74 382	18.44 386	60.86 366	39.66 382	15.11 377	9.53 381	22.10 378	6.52 381	46.94 380	8.31 380	10.14 378	13.98 378

tmp_tt		22.26 348	23.75 350	17.80 364	5.23 388	12.06 388	35.26 375	39.48 383	2.82 382	18.94 375	44.20 372	22.23 363	24.64 383	36.30 354	9.31 380	16.69 377

EMVS		23.76 347	23.20 351	25.46 363	41.52 383	16.90 387	60.56 367	38.79 385	14.62 378	8.99 382	20.24 381	7.35 380	45.82 381	7.25 381	9.46 379	13.64 379

MVE	24.84 23	24.35 346	19.77 352	38.09 360	34.56 386	26.92 381	26.57 376	38.87 384	11.73 379	11.37 380	27.44 376	1.37 387	50.42 379	11.41 378	14.60 377	36.93 373
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 350	10.95 353	12.33 365	48.05 380	19.89 385	25.89 377	1.92 389	3.58 381	3.12 383	1.37 383	0.64 388	15.77 384	6.23 382	7.77 382	1.35 380

ab-mvs-re		7.91 351	10.55 354	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	94.95 64	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

testmvs		7.23 352	9.62 355	0.06 367	0.04 389	0.02 391	84.98 288	0.02 390	0.03 384	0.18 385	1.21 384	0.01 390	0.02 385	0.14 383	0.01 383	0.13 382

test123		6.92 353	9.21 356	0.08 366	0.03 390	0.05 390	81.65 311	0.01 391	0.02 385	0.14 386	0.85 385	0.03 389	0.02 385	0.12 384	0.00 384	0.16 381

pcd_1.5k_mvsjas		4.46 354	5.95 357	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	53.55 205	0.00 387	0.00 385	0.00 384	0.00 383

test_blank		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

uanet_test		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

DCPMVS		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

sosnet-low-res		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

sosnet		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

uncertanet		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

Regformer		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

uanet		0.00 355	0.00 358	0.00 368	0.00 391	0.00 392	0.00 379	0.00 392	0.00 386	0.00 387	0.00 386	0.00 391	0.00 387	0.00 385	0.00 384	0.00 383

FOURS1					93.95 53	61.77 242	93.96 69	91.92 143	62.14 303	86.57 35

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 11		95.05 21					99.07 13	92.01 12	94.77 25	96.51 20

test_one_0601					96.32 20	69.74 42		94.18 55	71.42 207	90.67 15	96.85 12	74.45 19

eth-test2					0.00 391

eth-test					0.00 391

ZD-MVS					96.63 11	65.50 154		93.50 80	70.74 224	85.26 53	95.19 58	64.92 77	97.29 78	87.51 45	93.01 59

IU-MVS					96.46 13	69.91 37		95.18 14	80.75 37	95.28 1				92.34 8	95.36 14	96.47 24

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_241102_TWO								94.41 45	71.65 195	92.07 6	97.21 5	74.58 17	99.11 6	92.34 8	95.36 14	96.59 15

test_241102_ONE					96.45 14	69.38 47		94.44 43	71.65 195	92.11 4	97.05 8	76.79 9	99.11 6

save fliter					93.84 56	67.89 85	95.05 39	92.66 115	78.19 71

test_0728_THIRD									72.48 165	90.55 16	96.93 10	76.24 11	99.08 11	91.53 16	94.99 17	96.43 26

test_0728_SECOND				88.70 16	96.45 14	70.43 29	96.64 8	94.37 49					99.15 2	91.91 14	94.90 21	96.51 20

test0726					96.40 17	69.99 33	96.76 6	94.33 51	71.92 181	91.89 8	97.11 7	73.77 22

GSMVS																94.68 89

test_part2					96.29 21	68.16 79				90.78 13

sam_mvs1												57.85 151				94.68 89

sam_mvs												54.91 190

ambc				69.61 338	61.38 374	41.35 368	49.07 373	85.86 313		50.18 347	66.40 356	10.16 376	88.14 326	45.73 326	44.20 359	79.32 348

MTGPA								92.23 129

test_post1							78.95 333				20.70 380	53.05 211	91.50 297	60.43 269

test_post											23.01 377	56.49 172	92.67 262

patchmatchnet-post											67.62 355	57.62 154	90.25 305

GG-mvs-BLEND				86.53 65	91.91 113	69.67 45	75.02 347	94.75 31		78.67 121	90.85 154	77.91 7	94.56 197	72.25 165	93.74 47	95.36 57

MTMP							93.77 84	32.52 386

gm-plane-assit					88.42 193	67.04 113			78.62 68		91.83 141		97.37 72	76.57 134

test9_res														89.41 26	94.96 18	95.29 62

TEST9					94.18 46	67.28 104	94.16 56	93.51 78	71.75 193	85.52 48	95.33 47	68.01 44	97.27 82

test_8					94.19 45	67.19 107	94.15 59	93.42 85	71.87 186	85.38 51	95.35 46	68.19 41	96.95 106

agg_prior2														86.41 57	94.75 30	95.33 58

agg_prior					94.16 50	66.97 115		93.31 88		84.49 59			96.75 116

TestCases				72.46 327	79.57 315	51.42 342		68.61 369	51.25 348	45.88 355	81.23 279	19.86 367	86.58 339	38.98 350	57.01 334	79.39 346

test_prior4						67.18 109	93.92 75

test_prior2							95.10 37		75.40 110	85.25 54	95.61 40	67.94 45		87.47 46	94.77 25

test_prior				86.42 69	94.71 39	67.35 102		93.10 100					96.84 111			95.05 76

旧先验2							92.00 153		59.37 323	87.54 29			93.47 239	75.39 140

新几何2							91.41 176

新几何1				84.73 130	92.32 97	64.28 189		91.46 170	59.56 321	79.77 104	92.90 119	56.95 165	96.57 121	63.40 250	92.91 61	93.34 142

旧先验1					91.94 110	60.74 263		91.50 168			94.36 82	65.23 71			91.84 77	94.55 94

无先验							92.71 121	92.61 119	62.03 304				97.01 96	66.63 219		93.97 121

原ACMM2							92.01 150

原ACMM1				84.42 141	93.21 75	64.27 190		93.40 87	65.39 277	79.51 107	92.50 127	58.11 150	96.69 118	65.27 241	93.96 42	92.32 172

test222					89.77 160	61.60 246	89.55 237	89.42 246	56.83 335	77.28 133	92.43 131	52.76 216			91.14 91	93.09 152

testdata2													96.09 134	61.26 265

segment_acmp												65.94 64

testdata				81.34 220	89.02 179	57.72 303		89.84 232	58.65 326	85.32 52	94.09 94	57.03 160	93.28 241	69.34 193	90.56 97	93.03 154

testdata1							89.21 246		77.55 85

test12				87.09 44	94.60 41	68.86 59		92.91 106		82.67 76		65.44 70	97.55 64		93.69 50	94.84 85

plane_prior7					86.94 226	61.51 247

plane_prior6					87.23 221	62.32 233						50.66 233

plane_prior5								91.31 175					95.55 163	76.74 132	78.53 192	88.39 230

plane_prior4											89.14 179

plane_prior3						61.95 240			79.09 59	72.53 186

plane_prior2							93.13 103		78.81 65

plane_prior1					87.15 223

plane_prior						62.42 230	93.85 79		79.38 51						78.80 189

n2								0.00 392

nn								0.00 392

door-mid								66.01 373

lessismore_v0				73.72 318	72.93 356	47.83 357		61.72 377		45.86 357	73.76 337	28.63 351	89.81 313	47.75 319	31.37 373	83.53 303

LGP-MVS_train				79.56 263	84.31 269	59.37 283		89.73 237	69.49 239	64.86 270	88.42 184	38.65 304	94.30 206	72.56 162	72.76 232	85.01 291

test11								93.01 102

door								66.57 372

HQP5-MVS						63.66 207

HQP-NCC					87.54 215		94.06 64		79.80 45	74.18 165

ACMP_Plane					87.54 215		94.06 64		79.80 45	74.18 165

BP-MVS														77.63 129

HQP4-MVS										74.18 165			95.61 157			88.63 224

HQP3-MVS								91.70 160							78.90 187

HQP2-MVS												51.63 226

NP-MVS					87.41 218	63.04 217					90.30 165

MDTV_nov1_ep13_2view						59.90 276	80.13 328		67.65 261	72.79 181		54.33 198		59.83 273		92.58 166

ACMMP++_ref															71.63 240

ACMMP++															69.72 251

Test By Simon												54.21 199

ITE_SJBPF				70.43 337	74.44 351	47.06 360		77.32 351	60.16 317	54.04 331	83.53 243	23.30 361	84.01 348	43.07 334	61.58 315	80.21 343

DeepMVS_CX				34.71 361	51.45 378	24.73 383		28.48 387	31.46 371	17.49 377	52.75 366	5.80 382	42.60 382	18.18 374	19.42 375	36.81 374

High-resolution multi-view benchmark