High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 10	97.13 2	95.58 11	89.33 1	85.77 71	96.26 46	72.84 32	99.38 1	92.64 33	95.93 9	97.08 11

MM		90.87 2	91.52 2	88.92 15	92.12 105	71.10 27	97.02 3	96.04 6	88.70 2	91.57 19	96.19 48	70.12 49	98.91 21	96.83 2	95.06 17	96.76 15

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 169	76.68 2	97.29 1	95.35 17	82.87 37	91.58 18	97.22 8	79.93 5	99.10 9	83.12 128	97.64 2	97.94 1

DVP-MVS++		90.53 4	91.09 5	88.87 16	97.31 4	69.91 43	93.96 88	94.37 61	72.48 237	92.07 11	96.85 27	83.82 2	99.15 2	91.53 46	97.42 4	97.55 4

MSP-MVS		90.38 5	91.87 1	85.88 109	92.83 84	64.03 232	93.06 133	94.33 63	82.19 45	93.65 3	96.15 50	85.89 1	97.19 98	91.02 50	97.75 1	96.43 31
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MGCNet		90.32 6	90.90 7	88.55 23	94.05 49	70.23 37	97.00 5	93.73 82	87.30 4	92.15 8	96.15 50	66.38 75	98.94 20	96.71 3	94.67 33	96.47 28

CNVR-MVS		90.32 6	90.89 8	88.61 22	96.76 8	70.65 30	96.47 14	94.83 36	84.83 17	89.07 43	96.80 30	70.86 45	99.06 15	92.64 33	95.71 11	96.12 40

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 75	73.88 9	97.01 4	94.40 59	88.32 3	85.71 72	94.91 91	74.11 23	98.91 21	87.26 78	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
SED-MVS		89.94 9	90.36 10	88.70 18	96.45 12	69.38 59	96.89 6	94.44 55	71.65 267	92.11 9	97.21 9	76.79 9	99.11 6	92.34 35	95.36 14	97.62 2

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 167	93.00 80	58.16 372	96.72 9	94.41 57	86.50 9	90.25 34	97.83 1	75.46 16	98.67 29	92.78 32	95.49 13	97.32 6

patch_mono-2		89.71 11	90.99 6	85.85 112	96.04 25	63.70 247	95.04 42	95.19 22	86.74 8	91.53 20	95.15 84	73.86 24	97.58 69	93.38 27	92.00 75	96.28 37

CANet		89.61 12	89.99 12	88.46 24	94.39 43	69.71 52	96.53 13	93.78 75	86.89 7	89.68 40	95.78 57	65.94 80	99.10 9	92.99 30	93.91 46	96.58 21

DVP-MVS		89.41 13	89.73 14	88.45 25	96.40 15	69.99 39	96.64 10	94.52 51	71.92 253	90.55 30	96.93 21	73.77 25	99.08 11	91.91 41	94.90 22	96.29 35
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
HPM-MVS++		89.37 14	89.95 13	87.64 35	95.10 31	68.23 100	95.24 34	94.49 53	82.43 42	88.90 45	96.35 41	71.89 42	98.63 30	88.76 64	96.40 6	96.06 41

balanced_conf03		89.08 15	88.84 23	89.81 6	93.66 58	75.15 5	90.61 269	93.43 97	84.06 24	86.20 66	90.17 225	72.42 37	96.98 115	93.09 29	95.92 10	97.29 7

NCCC		89.07 16	89.46 15	87.91 28	96.60 10	69.05 74	96.38 15	94.64 46	84.42 21	86.74 61	96.20 47	66.56 74	98.76 27	89.03 63	94.56 34	95.92 49

MED-MVS		88.94 17	89.45 16	87.42 45	94.76 34	67.28 125	94.47 61	94.87 32	70.09 299	91.27 23	96.95 17	76.77 11	98.98 16	91.55 43	94.28 37	95.99 45

DPE-MVS		88.77 18	89.21 19	87.45 44	96.26 21	67.56 118	94.17 74	94.15 68	68.77 320	90.74 28	97.27 6	76.09 14	98.49 33	90.58 54	94.91 21	96.30 34
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TestfortrainingZip a		88.66 19	88.99 21	87.70 33	94.76 34	68.73 83	94.47 61	94.87 32	73.09 224	91.27 23	96.95 17	76.77 11	98.98 16	84.41 111	94.28 37	95.37 69

ME-MVS		88.25 20	88.55 27	87.33 50	96.33 18	67.28 125	93.93 90	94.81 37	70.09 299	88.91 44	96.95 17	70.12 49	98.73 28	91.55 43	94.28 37	95.99 45

fmvsm_l_conf0.5_n_9		88.24 21	89.36 17	84.85 156	88.15 230	61.94 297	95.65 25	89.70 293	85.54 12	92.07 11	97.33 5	67.51 66	97.27 93	96.23 5	92.07 74	95.35 73

fmvsm_s_conf0.5_n_9		88.14 22	89.21 19	84.92 151	89.29 180	61.41 314	92.97 138	88.36 348	86.96 6	91.49 21	97.49 3	69.48 54	97.46 76	97.00 1	89.88 111	95.89 50

SMA-MVS		88.14 22	88.29 31	87.67 34	93.21 72	68.72 85	93.85 96	94.03 71	74.18 198	91.74 15	96.67 33	65.61 85	98.42 37	89.24 60	96.08 7	95.88 51
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 22	87.61 41	89.71 7	92.06 108	76.72 1	95.75 20	93.26 103	83.86 25	89.55 41	96.06 52	53.55 264	97.89 51	91.10 48	93.31 57	94.54 131

TSAR-MVS + MP.		88.11 25	88.64 26	86.54 87	91.73 123	68.04 104	90.36 276	93.55 89	82.89 35	91.29 22	92.89 146	72.27 39	96.03 169	87.99 68	94.77 26	95.54 62
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_s_conf0.5_n_11		87.99 26	89.25 18	84.23 193	89.07 188	61.60 307	94.87 49	89.06 321	85.65 11	91.09 26	97.41 4	68.26 58	97.43 80	95.07 13	92.74 64	93.66 182

fmvsm_s_conf0.5_n_8		87.96 27	88.93 22	85.07 146	88.43 217	61.78 300	94.73 56	91.74 177	85.87 10	91.66 17	97.50 2	64.03 106	98.33 38	96.28 4	90.08 107	95.10 90

TSAR-MVS + GP.		87.96 27	88.37 30	86.70 71	93.51 66	65.32 188	95.15 37	93.84 74	78.17 129	85.93 70	94.80 94	75.80 15	98.21 40	89.38 57	88.78 123	96.59 19

DeepC-MVS_fast	79.48 2	87.95 29	88.00 35	87.79 31	95.86 28	68.32 94	95.74 21	94.11 69	83.82 26	83.49 97	96.19 48	64.53 101	98.44 35	83.42 127	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_10		87.93 30	88.67 25	85.71 119	88.69 199	63.71 245	94.56 59	90.22 269	85.04 15	92.27 6	97.05 12	63.67 114	98.15 42	95.09 12	91.39 87	95.27 81

xiu_mvs_v2_base		87.92 31	87.38 45	89.55 12	91.41 135	76.43 3	95.74 21	93.12 111	83.53 29	89.55 41	95.95 55	53.45 268	97.68 59	91.07 49	92.62 65	94.54 131

EPNet		87.84 32	88.38 29	86.23 99	93.30 69	66.05 167	95.26 33	94.84 35	87.09 5	88.06 48	94.53 100	66.79 71	97.34 86	83.89 118	91.68 81	95.29 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
lupinMVS		87.74 33	87.77 38	87.63 39	89.24 185	71.18 24	96.57 12	92.90 121	82.70 39	87.13 56	95.27 77	64.99 91	95.80 184	89.34 58	91.80 79	95.93 48

test_fmvsm_n_1920		87.69 34	88.50 28	85.27 139	87.05 265	63.55 254	93.69 106	91.08 219	84.18 23	90.17 36	97.04 14	67.58 65	97.99 46	95.72 8	90.03 108	94.26 150

fmvsm_l_conf0.5_n_3		87.54 35	88.29 31	85.30 136	86.92 275	62.63 280	95.02 44	90.28 264	84.95 16	90.27 33	96.86 25	65.36 87	97.52 74	94.93 15	90.03 108	95.76 54

APDe-MVS		87.54 35	87.84 37	86.65 74	96.07 24	66.30 162	94.84 51	93.78 75	69.35 309	88.39 47	96.34 42	67.74 64	97.66 64	90.62 53	93.44 55	96.01 44
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_6		87.50 37	88.72 24	83.84 205	86.89 277	60.04 348	95.05 40	92.17 156	84.80 18	92.27 6	96.37 39	64.62 98	96.54 141	94.43 19	91.86 77	94.94 99

fmvsm_l_conf0.5_n		87.49 38	88.19 33	85.39 130	86.95 270	64.37 220	94.30 71	88.45 346	80.51 70	92.70 4	96.86 25	69.98 51	97.15 103	95.83 7	88.08 131	94.65 124

SD-MVS		87.49 38	87.49 43	87.50 43	93.60 60	68.82 81	93.90 93	92.63 136	76.86 155	87.90 50	95.76 58	66.17 77	97.63 66	89.06 62	91.48 85	96.05 42
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
fmvsm_l_conf0.5_n_a		87.44 40	88.15 34	85.30 136	87.10 263	64.19 227	94.41 66	88.14 356	80.24 81	92.54 5	96.97 16	69.52 53	97.17 99	95.89 6	88.51 126	94.56 128

dcpmvs_2		87.37 41	87.55 42	86.85 62	95.04 33	68.20 101	90.36 276	90.66 244	79.37 104	81.20 121	93.67 130	74.73 18	96.55 140	90.88 51	92.00 75	95.82 52

alignmvs		87.28 42	86.97 49	88.24 27	91.30 137	71.14 26	95.61 26	93.56 88	79.30 105	87.07 58	95.25 79	68.43 56	96.93 123	87.87 69	84.33 182	96.65 17

train_agg		87.21 43	87.42 44	86.60 77	94.18 45	67.28 125	94.16 75	93.51 91	71.87 258	85.52 75	95.33 71	68.19 59	97.27 93	89.09 61	94.90 22	95.25 85

MG-MVS		87.11 44	86.27 62	89.62 8	97.79 1	76.27 4	94.96 46	94.49 53	78.74 120	83.87 93	92.94 144	64.34 102	96.94 121	75.19 205	94.09 42	95.66 57

SF-MVS		87.03 45	87.09 47	86.84 63	92.70 90	67.45 123	93.64 109	93.76 78	70.78 291	86.25 64	96.44 38	66.98 69	97.79 55	88.68 65	94.56 34	95.28 80

fmvsm_s_conf0.5_n_3		86.88 46	87.99 36	83.58 219	87.26 257	60.74 328	93.21 130	87.94 363	84.22 22	91.70 16	97.27 6	65.91 82	95.02 227	93.95 24	90.42 103	94.99 96

CSCG		86.87 47	86.26 63	88.72 17	95.05 32	70.79 29	93.83 101	95.33 18	68.48 324	77.63 180	94.35 109	73.04 30	98.45 34	84.92 103	93.71 51	96.92 14

sasdasda		86.85 48	86.25 64	88.66 20	91.80 121	71.92 16	93.54 114	91.71 180	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 66	84.34 180	97.11 9

canonicalmvs		86.85 48	86.25 64	88.66 20	91.80 121	71.92 16	93.54 114	91.71 180	80.26 78	87.55 53	95.25 79	63.59 118	96.93 123	88.18 66	84.34 180	97.11 9

UBG		86.83 50	86.70 55	87.20 52	93.07 78	69.81 47	93.43 122	95.56 13	81.52 52	81.50 116	92.12 167	73.58 28	96.28 153	84.37 112	85.20 169	95.51 63

PHI-MVS		86.83 50	86.85 54	86.78 67	93.47 67	65.55 183	95.39 31	95.10 25	71.77 263	85.69 73	96.52 35	62.07 143	98.77 26	86.06 91	95.60 12	96.03 43

SteuartSystems-ACMMP		86.82 52	86.90 52	86.58 80	90.42 154	66.38 159	96.09 17	93.87 73	77.73 138	84.01 92	95.66 60	63.39 121	97.94 47	87.40 76	93.55 54	95.42 65
Skip Steuart: Steuart Systems R&D Blog.
fmvsm_s_conf0.5_n_4		86.79 53	87.63 39	84.27 191	86.15 293	61.48 311	94.69 57	91.16 207	83.79 28	90.51 32	96.28 44	64.24 103	98.22 39	95.00 14	86.88 143	93.11 200

PVSNet_Blended		86.73 54	86.86 53	86.31 98	93.76 54	67.53 120	96.33 16	93.61 86	82.34 44	81.00 127	93.08 140	63.19 125	97.29 89	87.08 82	91.38 88	94.13 159

testing11		86.71 55	86.44 60	87.55 41	93.54 64	71.35 21	93.65 108	95.58 11	81.36 59	80.69 132	92.21 165	72.30 38	96.46 146	85.18 99	83.43 196	94.82 109

test_fmvsmconf_n		86.58 56	87.17 46	84.82 158	85.28 311	62.55 281	94.26 73	89.78 284	83.81 27	87.78 52	96.33 43	65.33 88	96.98 115	94.40 20	87.55 137	94.95 98

BP-MVS1		86.54 57	86.68 57	86.13 102	87.80 245	67.18 132	92.97 138	95.62 10	79.92 86	82.84 104	94.14 118	74.95 17	96.46 146	82.91 132	88.96 122	94.74 114

jason		86.40 58	86.17 66	87.11 55	86.16 292	70.54 32	95.71 24	92.19 153	82.00 47	84.58 85	94.34 110	61.86 146	95.53 209	87.76 70	90.89 96	95.27 81
jason: jason.
NormalMVS		86.39 59	86.66 58	85.60 124	92.12 105	65.95 172	94.88 47	90.83 232	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	91.15 92	93.93 170

fmvsm_s_conf0.5_n		86.39 59	86.91 51	84.82 158	87.36 256	63.54 255	94.74 53	90.02 277	82.52 40	90.14 37	96.92 23	62.93 132	97.84 54	95.28 11	82.26 207	93.07 203

fmvsm_s_conf0.5_n_5		86.38 61	86.94 50	84.71 169	84.67 323	63.29 260	94.04 84	89.99 279	82.88 36	87.85 51	96.03 53	62.89 134	96.36 150	94.15 21	89.95 110	94.48 140

SymmetryMVS		86.32 62	86.39 61	86.12 103	90.52 152	65.95 172	94.88 47	94.58 50	84.69 19	83.67 95	94.10 119	63.16 127	96.91 127	85.31 95	86.59 152	95.51 63

WTY-MVS		86.32 62	85.81 74	87.85 29	92.82 86	69.37 61	95.20 35	95.25 20	82.71 38	81.91 112	94.73 95	67.93 63	97.63 66	79.55 170	82.25 209	96.54 22

myMVS_eth3d28		86.31 64	86.15 67	86.78 67	93.56 62	70.49 33	92.94 141	95.28 19	82.47 41	78.70 170	92.07 169	72.45 36	95.41 211	82.11 141	85.78 162	94.44 142

MSLP-MVS++		86.27 65	85.91 73	87.35 48	92.01 112	68.97 77	95.04 42	92.70 127	79.04 115	81.50 116	96.50 37	58.98 191	96.78 131	83.49 126	93.93 45	96.29 35

VNet		86.20 66	85.65 78	87.84 30	93.92 51	69.99 39	95.73 23	95.94 7	78.43 125	86.00 69	93.07 141	58.22 201	97.00 111	85.22 97	84.33 182	96.52 23

MVS_111021_HR		86.19 67	85.80 75	87.37 47	93.17 74	69.79 48	93.99 87	93.76 78	79.08 112	78.88 166	93.99 124	62.25 141	98.15 42	85.93 92	91.15 92	94.15 158

SPE-MVS-test		86.14 68	87.01 48	83.52 220	92.63 92	59.36 360	95.49 28	91.92 166	80.09 82	85.46 77	95.53 66	61.82 148	95.77 188	86.77 86	93.37 56	95.41 66

ACMMP_NAP		86.05 69	85.80 75	86.80 66	91.58 127	67.53 120	91.79 207	93.49 94	74.93 188	84.61 84	95.30 73	59.42 181	97.92 48	86.13 89	94.92 20	94.94 99

testing99		86.01 70	85.47 80	87.63 39	93.62 59	71.25 23	93.47 120	95.23 21	80.42 73	80.60 134	91.95 176	71.73 43	96.50 144	80.02 167	82.22 210	95.13 88

ETV-MVS		86.01 70	86.11 68	85.70 120	90.21 159	67.02 139	93.43 122	91.92 166	81.21 61	84.13 91	94.07 123	60.93 157	95.63 198	89.28 59	89.81 112	94.46 141

testing91		85.93 72	85.31 84	87.78 32	93.59 61	71.47 19	93.50 117	95.08 28	80.26 78	80.53 137	91.93 177	70.43 47	96.51 143	80.32 165	82.13 212	95.37 69

APD-MVS		85.93 72	85.99 71	85.76 116	95.98 27	65.21 191	93.59 112	92.58 138	66.54 343	86.17 67	95.88 56	63.83 110	97.00 111	86.39 88	92.94 61	95.06 92
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PAPM		85.89 74	85.46 81	87.18 53	88.20 229	72.42 15	92.41 175	92.77 125	82.11 46	80.34 140	93.07 141	68.27 57	95.02 227	78.39 186	93.59 53	94.09 161

CS-MVS		85.80 75	86.65 59	83.27 232	92.00 113	58.92 364	95.31 32	91.86 171	79.97 83	84.82 83	95.40 69	62.26 140	95.51 210	86.11 90	92.08 73	95.37 69

fmvsm_s_conf0.5_n_a		85.75 76	86.09 69	84.72 167	85.73 304	63.58 252	93.79 102	89.32 304	81.42 57	90.21 35	96.91 24	62.41 139	97.67 61	94.48 18	80.56 235	92.90 209

test_fmvsmconf0.1_n		85.71 77	86.08 70	84.62 177	80.83 373	62.33 286	93.84 99	88.81 333	83.50 30	87.00 59	96.01 54	63.36 122	96.93 123	94.04 23	87.29 140	94.61 126

CDPH-MVS		85.71 77	85.46 81	86.46 89	94.75 38	67.19 130	93.89 94	92.83 123	70.90 287	83.09 102	95.28 75	63.62 116	97.36 84	80.63 161	94.18 41	94.84 105

casdiffmvs_mvg		85.66 79	85.18 86	87.09 56	88.22 228	69.35 62	93.74 105	91.89 169	81.47 53	80.10 143	91.45 189	64.80 96	96.35 151	87.23 79	87.69 135	95.58 60
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
fmvsm_s_conf0.1_n		85.61 80	85.93 72	84.68 171	82.95 355	63.48 257	94.03 86	89.46 298	81.69 50	89.86 38	96.74 31	61.85 147	97.75 57	94.74 17	82.01 214	92.81 213

MGCFI-Net		85.59 81	85.73 77	85.17 143	91.41 135	62.44 282	92.87 146	91.31 197	79.65 93	86.99 60	95.14 85	62.90 133	96.12 161	87.13 81	84.13 188	96.96 13

GDP-MVS		85.54 82	85.32 83	86.18 100	87.64 248	67.95 108	92.91 144	92.36 143	77.81 135	83.69 94	94.31 112	72.84 32	96.41 148	80.39 164	85.95 159	94.19 154

DeepC-MVS	77.85 3	85.52 83	85.24 85	86.37 94	88.80 197	66.64 153	92.15 185	93.68 84	81.07 63	76.91 192	93.64 131	62.59 136	98.44 35	85.50 93	92.84 63	94.03 165
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 84	84.87 92	86.84 63	88.25 226	69.07 71	93.04 135	91.76 176	81.27 60	80.84 130	92.07 169	64.23 104	96.06 167	84.98 102	87.43 139	95.39 67
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 85	85.08 88	86.06 104	93.09 77	65.65 179	93.89 94	93.41 99	73.75 209	79.94 145	94.68 97	60.61 162	98.03 45	82.63 136	93.72 50	94.52 133

fmvsm_s_conf0.5_n_7		85.24 86	86.69 56	80.91 303	84.52 328	60.10 346	93.35 125	90.35 257	83.41 31	86.54 63	96.27 45	60.50 163	90.02 387	94.84 16	90.38 104	92.61 217

MP-MVS-pluss		85.24 86	85.13 87	85.56 125	91.42 132	65.59 181	91.54 223	92.51 140	74.56 191	80.62 133	95.64 61	59.15 186	97.00 111	86.94 84	93.80 47	94.07 163
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
testing222		85.18 88	84.69 96	86.63 76	92.91 82	69.91 43	92.61 161	95.80 9	80.31 77	80.38 139	92.27 161	68.73 55	95.19 224	75.94 199	83.27 198	94.81 111

PAPR		85.15 89	84.47 97	87.18 53	96.02 26	68.29 95	91.85 205	93.00 117	76.59 166	79.03 162	95.00 86	61.59 149	97.61 68	78.16 187	89.00 121	95.63 58

fmvsm_s_conf0.5_n_2		85.06 90	85.60 79	83.44 226	86.92 275	60.53 335	94.41 66	87.31 371	83.30 32	88.72 46	96.72 32	54.28 256	97.75 57	94.07 22	84.68 179	92.04 240

MP-MVS		85.02 91	84.97 90	85.17 143	92.60 93	64.27 225	93.24 127	92.27 146	73.13 220	79.63 154	94.43 103	61.90 144	97.17 99	85.00 101	92.56 66	94.06 164
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
baseline		85.01 92	84.44 98	86.71 70	88.33 223	68.73 83	90.24 281	91.82 175	81.05 64	81.18 122	92.50 153	63.69 113	96.08 166	84.45 110	86.71 150	95.32 76

CHOSEN 1792x2688		84.98 93	83.45 118	89.57 11	89.94 164	75.14 6	92.07 191	92.32 144	81.87 48	75.68 201	88.27 259	60.18 167	98.60 31	80.46 163	90.27 106	94.96 97

MVSMamba_PlusPlus		84.97 94	83.65 111	88.93 14	90.17 160	74.04 8	87.84 339	92.69 130	62.18 382	81.47 118	87.64 273	71.47 44	96.28 153	84.69 105	94.74 31	96.47 28

E3new		84.94 95	84.36 100	86.69 73	89.06 189	69.31 63	92.68 158	91.29 202	80.72 67	81.03 125	92.14 166	61.89 145	95.91 173	84.59 107	85.85 161	94.86 101

viewmanbaseed2359cas		84.89 96	84.26 102	86.78 67	88.50 208	69.77 50	92.69 157	91.13 213	81.11 62	81.54 115	91.98 173	60.35 164	95.73 190	84.47 109	86.56 153	94.84 105

EIA-MVS		84.84 97	84.88 91	84.69 170	91.30 137	62.36 285	93.85 96	92.04 159	79.45 100	79.33 159	94.28 114	62.42 138	96.35 151	80.05 166	91.25 91	95.38 68

lecture		84.77 98	84.81 94	84.65 173	92.12 105	62.27 289	94.74 53	92.64 135	68.35 325	85.53 74	95.30 73	59.77 174	97.91 49	83.73 122	91.15 92	93.77 179

fmvsm_s_conf0.1_n_a		84.76 99	84.84 93	84.53 179	80.23 386	63.50 256	92.79 148	88.73 336	80.46 71	89.84 39	96.65 34	60.96 156	97.57 71	93.80 25	80.14 237	92.53 222

viewcassd2359sk11		84.74 100	84.11 103	86.64 75	88.57 202	69.20 69	92.61 161	91.23 204	80.58 68	80.85 129	91.96 174	61.39 151	95.89 175	84.28 113	85.49 166	94.82 109

HFP-MVS		84.73 101	84.40 99	85.72 118	93.75 56	65.01 197	93.50 117	93.19 107	72.19 247	79.22 160	94.93 89	59.04 189	97.67 61	81.55 150	92.21 69	94.49 139

MVS		84.66 102	82.86 140	90.06 2	90.93 144	74.56 7	87.91 337	95.54 14	68.55 322	72.35 258	94.71 96	59.78 173	98.90 23	81.29 156	94.69 32	96.74 16

GST-MVS		84.63 103	84.29 101	85.66 121	92.82 86	65.27 189	93.04 135	93.13 110	73.20 218	78.89 163	94.18 117	59.41 182	97.85 53	81.45 152	92.48 68	93.86 176

EC-MVSNet		84.53 104	85.04 89	83.01 238	89.34 176	61.37 315	94.42 65	91.09 217	77.91 133	83.24 98	94.20 116	58.37 199	95.40 212	85.35 94	91.41 86	92.27 234

E2		84.45 105	83.74 107	86.56 82	87.90 238	69.06 72	92.53 169	91.13 213	80.35 75	80.58 135	91.69 184	60.70 158	95.84 178	83.80 120	84.99 171	94.79 112

E3		84.45 105	83.74 107	86.56 82	87.90 238	69.06 72	92.53 169	91.13 213	80.35 75	80.58 135	91.69 184	60.70 158	95.84 178	83.80 120	84.99 171	94.79 112

fmvsm_s_conf0.1_n_2		84.40 107	84.78 95	83.27 232	85.25 312	60.41 338	94.13 78	85.69 396	83.05 34	87.99 49	96.37 39	52.75 273	97.68 59	93.75 26	84.05 189	91.71 248

ACMMPR		84.37 108	84.06 104	85.28 138	93.56 62	64.37 220	93.50 117	93.15 109	72.19 247	78.85 168	94.86 92	56.69 224	97.45 77	81.55 150	92.20 70	94.02 166

region2R		84.36 109	84.03 105	85.36 134	93.54 64	64.31 223	93.43 122	92.95 119	72.16 250	78.86 167	94.84 93	56.97 219	97.53 73	81.38 154	92.11 72	94.24 152

LFMVS		84.34 110	82.73 142	89.18 13	94.76 34	73.25 11	94.99 45	91.89 169	71.90 255	82.16 111	93.49 135	47.98 326	97.05 106	82.55 137	84.82 175	97.25 8

test_yl		84.28 111	83.16 131	87.64 35	94.52 41	69.24 67	95.78 18	95.09 26	69.19 312	81.09 123	92.88 147	57.00 217	97.44 78	81.11 158	81.76 218	96.23 38

DCV-MVSNet		84.28 111	83.16 131	87.64 35	94.52 41	69.24 67	95.78 18	95.09 26	69.19 312	81.09 123	92.88 147	57.00 217	97.44 78	81.11 158	81.76 218	96.23 38

diffmvs		84.28 111	83.83 106	85.61 123	87.40 254	68.02 105	90.88 254	89.24 307	80.54 69	81.64 114	92.52 152	59.83 172	94.52 256	87.32 77	85.11 170	94.29 149
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	84.28 111	83.36 124	87.02 59	92.22 100	67.74 113	84.65 368	94.50 52	79.15 109	82.23 110	87.93 268	66.88 70	96.94 121	80.53 162	82.20 211	96.39 33

ETVMVS		84.22 115	83.71 109	85.76 116	92.58 94	68.25 99	92.45 173	95.53 15	79.54 99	79.46 156	91.64 187	70.29 48	94.18 270	69.16 268	82.76 204	94.84 105

MAR-MVS		84.18 116	83.43 119	86.44 91	96.25 22	65.93 174	94.28 72	94.27 65	74.41 193	79.16 161	95.61 62	53.99 259	98.88 25	69.62 262	93.26 58	94.50 138
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		84.16 117	83.20 128	87.05 58	91.56 128	69.82 46	89.99 290	92.05 158	77.77 137	82.84 104	86.57 290	63.93 109	96.09 163	74.91 210	89.18 118	95.25 85

CANet_DTU		84.09 118	83.52 112	85.81 113	90.30 157	66.82 147	91.87 203	89.01 324	85.27 13	86.09 68	93.74 128	47.71 332	96.98 115	77.90 189	89.78 114	93.65 183

viewdifsd2359ckpt13		84.08 119	83.21 127	86.70 71	88.49 212	69.55 55	92.25 179	91.14 211	79.71 91	79.73 151	91.72 183	58.83 192	95.89 175	82.06 142	84.99 171	94.66 123

viewmacassd2359aftdt		84.03 120	83.18 130	86.59 79	86.76 278	69.44 56	92.44 174	90.85 231	80.38 74	80.78 131	91.33 195	58.54 196	95.62 200	82.15 140	85.41 167	94.72 117

ET-MVSNet_ETH3D		84.01 121	83.15 133	86.58 80	90.78 149	70.89 28	94.74 53	94.62 47	81.44 56	58.19 400	93.64 131	73.64 27	92.35 342	82.66 135	78.66 257	96.50 27

E4		84.00 122	83.19 129	86.46 89	86.99 266	68.85 79	92.39 176	90.99 226	79.94 84	80.17 142	91.36 194	59.73 175	95.79 185	82.87 133	84.22 186	94.74 114

diffmvs_AUTHOR		83.97 123	83.49 115	85.39 130	86.09 294	67.83 110	90.76 259	89.05 322	79.94 84	81.43 119	92.23 164	59.53 178	94.42 259	87.18 80	85.22 168	93.92 172

PVSNet_Blended_VisFu		83.97 123	83.50 114	85.39 130	90.02 162	66.59 156	93.77 103	91.73 178	77.43 147	77.08 191	89.81 235	63.77 112	96.97 118	79.67 169	88.21 129	92.60 218

MTAPA		83.91 125	83.38 123	85.50 126	91.89 119	65.16 193	81.75 399	92.23 147	75.32 183	80.53 137	95.21 82	56.06 233	97.16 102	84.86 104	92.55 67	94.18 155

XVS		83.87 126	83.47 117	85.05 147	93.22 70	63.78 239	92.92 142	92.66 132	73.99 201	78.18 174	94.31 112	55.25 239	97.41 81	79.16 176	91.58 83	93.95 168

Effi-MVS+		83.82 127	82.76 141	86.99 60	89.56 172	69.40 57	91.35 234	86.12 390	72.59 234	83.22 101	92.81 150	59.60 177	96.01 171	81.76 149	87.80 134	95.56 61

test_fmvsmvis_n_1920		83.80 128	83.48 116	84.77 162	82.51 358	63.72 244	91.37 232	83.99 414	81.42 57	77.68 179	95.74 59	58.37 199	97.58 69	93.38 27	86.87 144	93.00 206

EI-MVSNet-Vis-set		83.77 129	83.67 110	84.06 196	92.79 89	63.56 253	91.76 212	94.81 37	79.65 93	77.87 177	94.09 121	63.35 123	97.90 50	79.35 174	79.36 247	90.74 269

MVSFormer		83.75 130	82.88 139	86.37 94	89.24 185	71.18 24	89.07 315	90.69 241	65.80 349	87.13 56	94.34 110	64.99 91	92.67 328	72.83 227	91.80 79	95.27 81

CP-MVS		83.71 131	83.40 122	84.65 173	93.14 75	63.84 237	94.59 58	92.28 145	71.03 285	77.41 184	94.92 90	55.21 242	96.19 158	81.32 155	90.70 98	93.91 173

test_fmvsmconf0.01_n		83.70 132	83.52 112	84.25 192	75.26 434	61.72 304	92.17 184	87.24 373	82.36 43	84.91 82	95.41 68	55.60 237	96.83 130	92.85 31	85.87 160	94.21 153

baseline2		83.68 133	83.42 121	84.48 182	87.37 255	66.00 169	90.06 285	95.93 8	79.71 91	69.08 296	90.39 213	77.92 6	96.28 153	78.91 181	81.38 222	91.16 262

E6new		83.62 134	82.65 144	86.55 84	86.98 267	69.29 64	91.69 216	90.95 228	79.60 97	79.80 147	91.25 197	58.04 204	95.84 178	81.84 146	83.67 192	94.52 133

E6		83.62 134	82.65 144	86.55 84	86.98 267	69.29 64	91.69 216	90.95 228	79.60 97	79.80 147	91.25 197	58.04 204	95.84 178	81.84 146	83.67 192	94.52 133

E5		83.62 134	82.65 144	86.55 84	86.98 267	69.28 66	91.69 216	90.96 227	79.61 95	79.80 147	91.25 197	58.04 204	95.84 178	81.83 148	83.66 194	94.52 133

viewdifsd2359ckpt09		83.52 137	82.57 148	86.37 94	88.02 235	68.47 90	91.78 209	89.63 294	79.61 95	78.56 172	92.00 172	59.28 184	95.96 172	81.94 144	82.35 205	94.69 118

reproduce-ours		83.51 138	83.33 125	84.06 196	92.18 103	60.49 336	90.74 261	92.04 159	64.35 359	83.24 98	95.59 64	59.05 187	97.27 93	83.61 123	89.17 119	94.41 147

our_new_method		83.51 138	83.33 125	84.06 196	92.18 103	60.49 336	90.74 261	92.04 159	64.35 359	83.24 98	95.59 64	59.05 187	97.27 93	83.61 123	89.17 119	94.41 147

thisisatest0515		83.41 140	82.49 150	86.16 101	89.46 175	68.26 97	93.54 114	94.70 43	74.31 196	75.75 199	90.92 203	72.62 34	96.52 142	69.64 260	81.50 221	93.71 180

PVSNet_BlendedMVS		83.38 141	83.43 119	83.22 234	93.76 54	67.53 120	94.06 80	93.61 86	79.13 110	81.00 127	85.14 308	63.19 125	97.29 89	87.08 82	73.91 295	84.83 372

test2506		83.29 142	82.92 138	84.37 186	88.39 220	63.18 266	92.01 194	91.35 196	77.66 140	78.49 173	91.42 190	64.58 100	95.09 226	73.19 223	89.23 116	94.85 102

PGM-MVS		83.25 143	82.70 143	84.92 151	92.81 88	64.07 231	90.44 271	92.20 151	71.28 279	77.23 188	94.43 103	55.17 243	97.31 88	79.33 175	91.38 88	93.37 190

HPM-MVS		83.25 143	82.95 137	84.17 194	92.25 99	62.88 275	90.91 251	91.86 171	70.30 296	77.12 189	93.96 125	56.75 222	96.28 153	82.04 143	91.34 90	93.34 191
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
reproduce_model		83.15 145	82.96 135	83.73 211	92.02 109	59.74 352	90.37 275	92.08 157	63.70 366	82.86 103	95.48 67	58.62 194	97.17 99	83.06 129	88.42 127	94.26 150

EI-MVSNet-UG-set		83.14 146	82.96 135	83.67 216	92.28 98	63.19 265	91.38 231	94.68 44	79.22 107	76.60 194	93.75 127	62.64 135	97.76 56	78.07 188	78.01 260	90.05 278

testing3-2		83.11 147	83.15 133	82.98 239	91.92 116	64.01 233	94.39 69	95.37 16	78.32 126	75.53 206	90.06 231	73.18 29	93.18 307	74.34 215	75.27 284	91.77 247

VDD-MVS		83.06 148	81.81 161	86.81 65	90.86 147	67.70 114	95.40 30	91.50 191	75.46 178	81.78 113	92.34 160	40.09 377	97.13 104	86.85 85	82.04 213	95.60 59

h-mvs33		83.01 149	82.56 149	84.35 187	89.34 176	62.02 293	92.72 151	93.76 78	81.45 54	82.73 107	92.25 163	60.11 168	97.13 104	87.69 71	62.96 380	93.91 173

PAPM_NR		82.97 150	81.84 160	86.37 94	94.10 48	66.76 150	87.66 343	92.84 122	69.96 302	74.07 231	93.57 133	63.10 130	97.50 75	70.66 255	90.58 100	94.85 102

mPP-MVS		82.96 151	82.44 151	84.52 180	92.83 84	62.92 273	92.76 149	91.85 173	71.52 275	75.61 204	94.24 115	53.48 267	96.99 114	78.97 179	90.73 97	93.64 184

viewdifsd2359ckpt07		82.95 152	82.04 155	85.66 121	87.19 260	66.73 151	91.56 222	90.39 256	77.58 143	77.58 183	91.19 200	58.57 195	95.65 197	82.32 138	82.01 214	94.60 127

SR-MVS		82.81 153	82.58 147	83.50 223	93.35 68	61.16 318	92.23 182	91.28 203	64.48 358	81.27 120	95.28 75	53.71 263	95.86 177	82.87 133	88.77 124	93.49 188

DP-MVS Recon		82.73 154	81.65 162	85.98 106	97.31 4	67.06 135	95.15 37	91.99 163	69.08 317	76.50 196	93.89 126	54.48 252	98.20 41	70.76 253	85.66 164	92.69 214

CLD-MVS		82.73 154	82.35 153	83.86 204	87.90 238	67.65 116	95.45 29	92.18 154	85.06 14	72.58 249	92.27 161	52.46 276	95.78 186	84.18 114	79.06 252	88.16 306
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
sss		82.71 156	82.38 152	83.73 211	89.25 182	59.58 355	92.24 181	94.89 31	77.96 131	79.86 146	92.38 158	56.70 223	97.05 106	77.26 192	80.86 230	94.55 129

3Dnovator	73.91 6	82.69 157	80.82 175	88.31 26	89.57 171	71.26 22	92.60 163	94.39 60	78.84 117	67.89 318	92.48 156	48.42 321	98.52 32	68.80 273	94.40 36	95.15 87

RRT-MVS		82.61 158	81.16 166	86.96 61	91.10 141	68.75 82	87.70 342	92.20 151	76.97 153	72.68 245	87.10 284	51.30 290	96.41 148	83.56 125	87.84 133	95.74 55

viewmambaseed2359dif		82.60 159	81.91 159	84.67 172	85.83 301	66.09 166	90.50 270	89.01 324	75.46 178	79.64 153	92.01 171	59.51 179	94.38 261	82.99 131	82.26 207	93.54 186

MVSTER		82.47 160	82.05 154	83.74 209	92.68 91	69.01 75	91.90 202	93.21 104	79.83 87	72.14 259	85.71 303	74.72 19	94.72 241	75.72 201	72.49 305	87.50 313

TESTMET0.1,1		82.41 161	81.98 158	83.72 213	88.08 231	63.74 241	92.70 153	93.77 77	79.30 105	77.61 181	87.57 275	58.19 202	94.08 275	73.91 217	86.68 151	93.33 193

CostFormer		82.33 162	81.15 167	85.86 111	89.01 192	68.46 91	82.39 396	93.01 115	75.59 176	80.25 141	81.57 354	72.03 41	94.96 231	79.06 178	77.48 268	94.16 157

API-MVS		82.28 163	80.53 184	87.54 42	96.13 23	70.59 31	93.63 110	91.04 224	65.72 351	75.45 207	92.83 149	56.11 232	98.89 24	64.10 324	89.75 115	93.15 198

IB-MVS	77.80 4	82.18 164	80.46 186	87.35 48	89.14 187	70.28 36	95.59 27	95.17 24	78.85 116	70.19 284	85.82 301	70.66 46	97.67 61	72.19 239	66.52 349	94.09 161
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		82.16 165	81.12 168	85.26 140	86.42 285	68.72 85	92.59 165	90.44 253	73.12 221	84.20 88	94.36 105	38.04 390	95.73 190	84.12 115	86.81 145	91.33 255

xiu_mvs_v1_base		82.16 165	81.12 168	85.26 140	86.42 285	68.72 85	92.59 165	90.44 253	73.12 221	84.20 88	94.36 105	38.04 390	95.73 190	84.12 115	86.81 145	91.33 255

xiu_mvs_v1_base_debi		82.16 165	81.12 168	85.26 140	86.42 285	68.72 85	92.59 165	90.44 253	73.12 221	84.20 88	94.36 105	38.04 390	95.73 190	84.12 115	86.81 145	91.33 255

3Dnovator+	73.60 7	82.10 168	80.60 182	86.60 77	90.89 146	66.80 149	95.20 35	93.44 96	74.05 200	67.42 325	92.49 155	49.46 311	97.65 65	70.80 252	91.68 81	95.33 74

MVS_111021_LR		82.02 169	81.52 163	83.51 222	88.42 218	62.88 275	89.77 293	88.93 329	76.78 158	75.55 205	93.10 138	50.31 300	95.38 214	83.82 119	87.02 142	92.26 235

PMMVS		81.98 170	82.04 155	81.78 275	89.76 168	56.17 393	91.13 247	90.69 241	77.96 131	80.09 144	93.57 133	46.33 346	94.99 230	81.41 153	87.46 138	94.17 156

baseline1		81.84 171	81.03 172	84.28 190	91.60 126	66.62 154	91.08 248	91.66 185	81.87 48	74.86 217	91.67 186	69.98 51	94.92 234	71.76 242	64.75 366	91.29 260

EPP-MVSNet		81.79 172	81.52 163	82.61 249	88.77 198	60.21 344	93.02 137	93.66 85	68.52 323	72.90 243	90.39 213	72.19 40	94.96 231	74.93 209	79.29 250	92.67 215

WBMVS		81.67 173	80.98 174	83.72 213	93.07 78	69.40 57	94.33 70	93.05 113	76.84 156	72.05 261	84.14 320	74.49 21	93.88 289	72.76 230	68.09 335	87.88 308

test_vis1_n_1920		81.66 174	82.01 157	80.64 306	82.24 360	55.09 402	94.76 52	86.87 377	81.67 51	84.40 87	94.63 98	38.17 387	94.67 247	91.98 40	83.34 197	92.16 238

APD-MVS_3200maxsize		81.64 175	81.32 165	82.59 251	92.36 96	58.74 366	91.39 229	91.01 225	63.35 370	79.72 152	94.62 99	51.82 279	96.14 160	79.71 168	87.93 132	92.89 210

mvsmamba		81.55 176	80.72 177	84.03 200	91.42 132	66.93 145	83.08 387	89.13 315	78.55 124	67.50 323	87.02 285	51.79 281	90.07 386	87.48 74	90.49 102	95.10 90

ACMMP		81.49 177	80.67 179	83.93 202	91.71 124	62.90 274	92.13 186	92.22 150	71.79 262	71.68 267	93.49 135	50.32 299	96.96 119	78.47 185	84.22 186	91.93 245
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
KinetiMVS		81.43 178	80.11 188	85.38 133	86.60 281	65.47 187	92.90 145	93.54 90	75.33 182	77.31 186	90.39 213	46.81 338	96.75 132	71.65 245	86.46 156	93.93 170

CDS-MVSNet		81.43 178	80.74 176	83.52 220	86.26 289	64.45 214	92.09 189	90.65 245	75.83 174	73.95 233	89.81 235	63.97 108	92.91 318	71.27 246	82.82 201	93.20 197
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		81.36 180	79.99 192	85.46 127	90.39 156	68.40 92	86.88 354	90.61 246	74.41 193	70.31 283	84.67 313	63.79 111	92.32 344	73.13 224	85.70 163	95.67 56

ECVR-MVS		81.29 181	80.38 187	84.01 201	88.39 220	61.96 295	92.56 168	86.79 379	77.66 140	76.63 193	91.42 190	46.34 345	95.24 223	74.36 214	89.23 116	94.85 102

guyue		81.23 182	80.57 183	83.21 236	86.64 279	61.85 298	92.52 171	92.78 124	78.69 121	74.92 216	89.42 239	50.07 303	95.35 215	80.79 160	79.31 249	92.42 224

IMVS_0403		81.19 183	79.88 194	85.13 145	88.54 203	64.75 202	88.84 320	90.80 235	76.73 161	75.21 210	90.18 219	54.22 257	96.21 157	73.47 219	80.95 225	94.43 143

thisisatest0530		81.15 184	80.07 189	84.39 185	88.26 225	65.63 180	91.40 227	94.62 47	71.27 280	70.93 274	89.18 244	72.47 35	96.04 168	65.62 313	76.89 275	91.49 251

Fast-Effi-MVS+		81.14 185	80.01 191	84.51 181	90.24 158	65.86 175	94.12 79	89.15 313	73.81 208	75.37 209	88.26 260	57.26 212	94.53 255	66.97 297	84.92 174	93.15 198

HQP-MVS		81.14 185	80.64 180	82.64 248	87.54 250	63.66 250	94.06 80	91.70 183	79.80 88	74.18 224	90.30 216	51.63 284	95.61 201	77.63 190	78.90 253	88.63 297

hse-mvs2		81.12 187	81.11 171	81.16 291	86.52 284	57.48 381	89.40 306	91.16 207	81.45 54	82.73 107	90.49 211	60.11 168	94.58 248	87.69 71	60.41 407	91.41 254

SR-MVS-dyc-post		81.06 188	80.70 178	82.15 266	92.02 109	58.56 369	90.90 252	90.45 249	62.76 377	78.89 163	94.46 101	51.26 291	95.61 201	78.77 183	86.77 148	92.28 231

HyFIR lowres test		81.03 189	79.56 201	85.43 128	87.81 244	68.11 103	90.18 282	90.01 278	70.65 293	72.95 242	86.06 297	63.61 117	94.50 257	75.01 208	79.75 241	93.67 181

nrg030		80.93 190	79.86 195	84.13 195	83.69 344	68.83 80	93.23 128	91.20 205	75.55 177	75.06 212	88.22 263	63.04 131	94.74 240	81.88 145	66.88 346	88.82 295

Vis-MVSNet		80.92 191	79.98 193	83.74 209	88.48 214	61.80 299	93.44 121	88.26 355	73.96 204	77.73 178	91.76 180	49.94 305	94.76 238	65.84 309	90.37 105	94.65 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test1111		80.84 192	80.02 190	83.33 227	87.87 241	60.76 326	92.62 160	86.86 378	77.86 134	75.73 200	91.39 192	46.35 344	94.70 246	72.79 229	88.68 125	94.52 133

UWE-MVS		80.81 193	81.01 173	80.20 316	89.33 178	57.05 387	91.91 201	94.71 42	75.67 175	75.01 213	89.37 240	63.13 129	91.44 369	67.19 294	82.80 203	92.12 239

IMVS_0407		80.80 194	79.39 207	85.00 150	88.54 203	64.75 202	88.40 328	90.80 235	76.73 161	73.95 233	90.18 219	51.55 286	95.81 183	73.47 219	80.95 225	94.43 143

1314		80.70 195	78.95 215	85.94 108	87.77 247	67.56 118	87.91 337	92.55 139	72.17 249	67.44 324	93.09 139	50.27 301	97.04 109	71.68 244	87.64 136	93.23 195

AstraMVS		80.66 196	79.79 197	83.28 231	85.07 318	61.64 306	92.19 183	90.58 247	79.40 102	74.77 219	90.18 219	45.93 350	95.61 201	83.04 130	76.96 274	92.60 218

tpmrst		80.57 197	79.14 213	84.84 157	90.10 161	68.28 96	81.70 400	89.72 291	77.63 142	75.96 198	79.54 386	64.94 93	92.71 325	75.43 203	77.28 271	93.55 185

1112_ss		80.56 198	79.83 196	82.77 243	88.65 200	60.78 324	92.29 178	88.36 348	72.58 235	72.46 255	94.95 87	65.09 90	93.42 303	66.38 303	77.71 262	94.10 160

VDDNet		80.50 199	78.26 223	87.21 51	86.19 290	69.79 48	94.48 60	91.31 197	60.42 398	79.34 158	90.91 204	38.48 385	96.56 139	82.16 139	81.05 224	95.27 81

BH-w/o		80.49 200	79.30 209	84.05 199	90.83 148	64.36 222	93.60 111	89.42 301	74.35 195	69.09 295	90.15 227	55.23 241	95.61 201	64.61 321	86.43 157	92.17 237

test_cas_vis1_n_1920		80.45 201	80.61 181	79.97 325	78.25 413	57.01 389	94.04 84	88.33 350	79.06 114	82.81 106	93.70 129	38.65 382	91.63 360	90.82 52	79.81 239	91.27 261

icg_test_0407_2		80.38 202	79.22 211	83.88 203	88.54 203	64.75 202	86.79 355	90.80 235	76.73 161	73.95 233	90.18 219	51.55 286	92.45 337	73.47 219	80.95 225	94.43 143

TAMVS		80.37 203	79.45 204	83.13 237	85.14 315	63.37 258	91.23 241	90.76 240	74.81 190	72.65 247	88.49 253	60.63 161	92.95 313	69.41 264	81.95 216	93.08 202

HQP_MVS		80.34 204	79.75 198	82.12 268	86.94 271	62.42 283	93.13 131	91.31 197	78.81 118	72.53 250	89.14 246	50.66 296	95.55 207	76.74 193	78.53 258	88.39 303

SDMVSNet		80.26 205	78.88 216	84.40 184	89.25 182	67.63 117	85.35 364	93.02 114	76.77 159	70.84 275	87.12 282	47.95 329	96.09 163	85.04 100	74.55 286	89.48 288

HPM-MVS_fast		80.25 206	79.55 203	82.33 258	91.55 129	59.95 349	91.32 236	89.16 312	65.23 355	74.71 221	93.07 141	47.81 331	95.74 189	74.87 212	88.23 128	91.31 259

ab-mvs		80.18 207	78.31 222	85.80 114	88.44 216	65.49 186	83.00 390	92.67 131	71.82 261	77.36 185	85.01 309	54.50 249	96.59 136	76.35 198	75.63 282	95.32 76

IS-MVSNet		80.14 208	79.41 205	82.33 258	87.91 237	60.08 347	91.97 198	88.27 353	72.90 230	71.44 271	91.73 182	61.44 150	93.66 298	62.47 338	86.53 154	93.24 194

test-LLR		80.10 209	79.56 201	81.72 277	86.93 273	61.17 316	92.70 153	91.54 188	71.51 276	75.62 202	86.94 286	53.83 260	92.38 339	72.21 237	84.76 177	91.60 249

PVSNet	73.49 8	80.05 210	78.63 218	84.31 188	90.92 145	64.97 198	92.47 172	91.05 223	79.18 108	72.43 256	90.51 210	37.05 402	94.06 277	68.06 281	86.00 158	93.90 175

UA-Net		80.02 211	79.65 199	81.11 294	89.33 178	57.72 376	86.33 360	89.00 328	77.44 146	81.01 126	89.15 245	59.33 183	95.90 174	61.01 345	84.28 184	89.73 284

test-mter		79.96 212	79.38 208	81.72 277	86.93 273	61.17 316	92.70 153	91.54 188	73.85 206	75.62 202	86.94 286	49.84 307	92.38 339	72.21 237	84.76 177	91.60 249

QAPM		79.95 213	77.39 244	87.64 35	89.63 170	71.41 20	93.30 126	93.70 83	65.34 354	67.39 327	91.75 181	47.83 330	98.96 19	57.71 361	89.81 112	92.54 221

UGNet		79.87 214	78.68 217	83.45 225	89.96 163	61.51 309	92.13 186	90.79 239	76.83 157	78.85 168	86.33 294	38.16 388	96.17 159	67.93 284	87.17 141	92.67 215
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		79.80 215	77.95 230	85.34 135	88.28 224	68.26 97	81.56 402	91.42 194	70.11 298	77.59 182	80.50 372	67.40 67	94.26 268	67.34 291	77.35 269	93.51 187

thres200		79.66 216	78.33 221	83.66 217	92.54 95	65.82 177	93.06 133	96.31 3	74.90 189	73.30 239	88.66 251	59.67 176	95.61 201	47.84 405	78.67 256	89.56 287

CPTT-MVS		79.59 217	79.16 212	80.89 304	91.54 130	59.80 351	92.10 188	88.54 345	60.42 398	72.96 241	93.28 137	48.27 322	92.80 322	78.89 182	86.50 155	90.06 277

Test_1112_low_res		79.56 218	78.60 219	82.43 253	88.24 227	60.39 340	92.09 189	87.99 360	72.10 251	71.84 263	87.42 277	64.62 98	93.04 309	65.80 310	77.30 270	93.85 177

tttt0517		79.50 219	78.53 220	82.41 256	87.22 259	61.43 313	89.75 294	94.76 39	69.29 310	67.91 316	88.06 267	72.92 31	95.63 198	62.91 334	73.90 296	90.16 276

reproduce_monomvs		79.49 220	79.11 214	80.64 306	92.91 82	61.47 312	91.17 246	93.28 102	83.09 33	64.04 357	82.38 340	66.19 76	94.57 250	81.19 157	57.71 415	85.88 355

FIs		79.47 221	79.41 205	79.67 333	85.95 297	59.40 357	91.68 219	93.94 72	78.06 130	68.96 301	88.28 258	66.61 73	91.77 356	66.20 306	74.99 285	87.82 309

SSM_0404		79.46 222	77.65 234	84.91 153	88.37 222	67.04 137	89.59 295	87.03 374	67.99 328	75.45 207	89.32 241	47.98 326	95.34 217	71.23 247	81.90 217	92.34 227

BH-RMVSNet		79.46 222	77.65 234	84.89 154	91.68 125	65.66 178	93.55 113	88.09 358	72.93 227	73.37 238	91.12 202	46.20 348	96.12 161	56.28 367	85.61 165	92.91 208

viewdifsd2359ckpt11		79.42 224	77.95 230	83.81 206	83.87 341	63.85 235	89.54 300	87.38 367	77.39 149	74.94 214	89.95 232	51.11 292	94.72 241	79.52 171	67.90 338	92.88 211

viewmsd2359difaftdt		79.42 224	77.96 229	83.81 206	83.88 340	63.85 235	89.54 300	87.38 367	77.39 149	74.94 214	89.95 232	51.11 292	94.72 241	79.52 171	67.90 338	92.88 211

PCF-MVS	73.15 9	79.29 226	77.63 236	84.29 189	86.06 295	65.96 171	87.03 350	91.10 216	69.86 304	69.79 291	90.64 206	57.54 211	96.59 136	64.37 323	82.29 206	90.32 274
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		79.24 227	79.57 200	78.24 354	88.46 215	52.29 413	90.41 273	89.12 316	74.24 197	69.13 294	91.91 178	65.77 83	90.09 385	59.00 357	88.09 130	92.33 228

114514_t		79.17 228	77.67 233	83.68 215	95.32 30	65.53 184	92.85 147	91.60 187	63.49 368	67.92 315	90.63 208	46.65 341	95.72 195	67.01 296	83.54 195	89.79 282

FA-MVS(test-final)		79.12 229	77.23 246	84.81 161	90.54 151	63.98 234	81.35 405	91.71 180	71.09 284	74.85 218	82.94 333	52.85 271	97.05 106	67.97 282	81.73 220	93.41 189

SSM_0407		79.09 230	77.21 247	84.75 165	88.50 208	66.98 141	89.21 311	87.03 374	67.99 328	74.12 228	89.32 241	47.98 326	95.29 222	71.23 247	79.52 242	91.98 242

VPA-MVSNet		79.03 231	78.00 227	82.11 271	85.95 297	64.48 213	93.22 129	94.66 45	75.05 187	74.04 232	84.95 310	52.17 278	93.52 300	74.90 211	67.04 345	88.32 305

OPM-MVS		79.00 232	78.09 225	81.73 276	83.52 347	63.83 238	91.64 221	90.30 262	76.36 170	71.97 262	89.93 234	46.30 347	95.17 225	75.10 206	77.70 263	86.19 343
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
EI-MVSNet		78.97 233	78.22 224	81.25 288	85.33 308	62.73 278	89.53 303	93.21 104	72.39 242	72.14 259	90.13 228	60.99 154	94.72 241	67.73 286	72.49 305	86.29 340

AdaColmap		78.94 234	77.00 251	84.76 164	96.34 17	65.86 175	92.66 159	87.97 362	62.18 382	70.56 277	92.37 159	43.53 362	97.35 85	64.50 322	82.86 200	91.05 264

GeoE		78.90 235	77.43 240	83.29 230	88.95 193	62.02 293	92.31 177	86.23 386	70.24 297	71.34 272	89.27 243	54.43 253	94.04 280	63.31 330	80.81 232	93.81 178

miper_enhance_ethall		78.86 236	77.97 228	81.54 283	88.00 236	65.17 192	91.41 225	89.15 313	75.19 185	68.79 304	83.98 323	67.17 68	92.82 320	72.73 231	65.30 356	86.62 335

VPNet		78.82 237	77.53 239	82.70 246	84.52 328	66.44 158	93.93 90	92.23 147	80.46 71	72.60 248	88.38 257	49.18 315	93.13 308	72.47 235	63.97 376	88.55 300

EPNet_dtu		78.80 238	79.26 210	77.43 362	88.06 232	49.71 430	91.96 199	91.95 165	77.67 139	76.56 195	91.28 196	58.51 197	90.20 383	56.37 366	80.95 225	92.39 225
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		78.79 239	77.43 240	82.88 241	92.21 101	64.49 211	92.05 192	96.28 4	73.48 215	71.75 265	88.26 260	60.07 170	95.32 218	45.16 418	77.58 265	88.83 293

TR-MVS		78.77 240	77.37 245	82.95 240	90.49 153	60.88 322	93.67 107	90.07 273	70.08 301	74.51 222	91.37 193	45.69 351	95.70 196	60.12 351	80.32 236	92.29 230

thres400		78.68 241	77.43 240	82.43 253	92.21 101	64.49 211	92.05 192	96.28 4	73.48 215	71.75 265	88.26 260	60.07 170	95.32 218	45.16 418	77.58 265	87.48 314

BH-untuned		78.68 241	77.08 248	83.48 224	89.84 165	63.74 241	92.70 153	88.59 342	71.57 273	66.83 334	88.65 252	51.75 282	95.39 213	59.03 356	84.77 176	91.32 258

OMC-MVS		78.67 243	77.91 232	80.95 301	85.76 303	57.40 383	88.49 326	88.67 339	73.85 206	72.43 256	92.10 168	49.29 314	94.55 254	72.73 231	77.89 261	90.91 268

tpm		78.58 244	77.03 249	83.22 234	85.94 299	64.56 209	83.21 386	91.14 211	78.31 127	73.67 236	79.68 384	64.01 107	92.09 350	66.07 307	71.26 315	93.03 204

OpenMVS	70.45 11	78.54 245	75.92 269	86.41 93	85.93 300	71.68 18	92.74 150	92.51 140	66.49 344	64.56 351	91.96 174	43.88 361	98.10 44	54.61 372	90.65 99	89.44 290

EPMVS		78.49 246	75.98 268	86.02 105	91.21 139	69.68 53	80.23 414	91.20 205	75.25 184	72.48 254	78.11 395	54.65 248	93.69 297	57.66 362	83.04 199	94.69 118

AUN-MVS		78.37 247	77.43 240	81.17 290	86.60 281	57.45 382	89.46 305	91.16 207	74.11 199	74.40 223	90.49 211	55.52 238	94.57 250	74.73 213	60.43 406	91.48 252

thres100view900		78.37 247	77.01 250	82.46 252	91.89 119	63.21 264	91.19 245	96.33 1	72.28 245	70.45 280	87.89 269	60.31 165	95.32 218	45.16 418	77.58 265	88.83 293

GA-MVS		78.33 249	76.23 264	84.65 173	83.65 345	66.30 162	91.44 224	90.14 271	76.01 172	70.32 282	84.02 322	42.50 366	94.72 241	70.98 250	77.00 273	92.94 207

cascas		78.18 250	75.77 271	85.41 129	87.14 262	69.11 70	92.96 140	91.15 210	66.71 342	70.47 278	86.07 296	37.49 396	96.48 145	70.15 258	79.80 240	90.65 270

UniMVSNet_NR-MVSNet		78.15 251	77.55 238	79.98 323	84.46 331	60.26 342	92.25 179	93.20 106	77.50 145	68.88 302	86.61 289	66.10 78	92.13 348	66.38 303	62.55 384	87.54 312

LuminaMVS		78.14 252	76.66 255	82.60 250	80.82 374	64.64 208	89.33 307	90.45 249	68.25 326	74.73 220	85.51 305	41.15 372	94.14 271	78.96 180	80.69 234	89.04 291

IMVS_0404		78.11 253	76.29 263	83.59 218	88.54 203	64.75 202	84.63 369	90.80 235	76.73 161	61.16 380	90.18 219	40.17 376	91.58 362	73.47 219	80.95 225	94.43 143

thres600view7		78.00 254	76.66 255	82.03 273	91.93 115	63.69 248	91.30 237	96.33 1	72.43 240	70.46 279	87.89 269	60.31 165	94.92 234	42.64 430	76.64 276	87.48 314

FC-MVSNet-test		77.99 255	78.08 226	77.70 357	84.89 321	55.51 399	90.27 279	93.75 81	76.87 154	66.80 335	87.59 274	65.71 84	90.23 382	62.89 335	73.94 294	87.37 317

Anonymous202405211		77.96 256	75.33 277	85.87 110	93.73 57	64.52 210	94.85 50	85.36 399	62.52 380	76.11 197	90.18 219	29.43 435	97.29 89	68.51 275	77.24 272	95.81 53

cl22		77.94 257	76.78 253	81.42 285	87.57 249	64.93 200	90.67 264	88.86 332	72.45 239	67.63 322	82.68 337	64.07 105	92.91 318	71.79 240	65.30 356	86.44 337

XXY-MVS		77.94 257	76.44 258	82.43 253	82.60 357	64.44 215	92.01 194	91.83 174	73.59 214	70.00 287	85.82 301	54.43 253	94.76 238	69.63 261	68.02 337	88.10 307

MS-PatchMatch		77.90 259	76.50 257	82.12 268	85.99 296	69.95 42	91.75 214	92.70 127	73.97 203	62.58 374	84.44 317	41.11 373	95.78 186	63.76 327	92.17 71	80.62 420

FE-MVSNET3		77.89 260	76.39 261	82.40 257	81.92 365	67.01 140	91.94 200	93.00 117	77.01 152	68.44 311	84.15 319	54.78 247	93.25 305	65.76 311	70.53 318	86.94 326

FMVSNet3		77.73 261	76.04 267	82.80 242	91.20 140	68.99 76	91.87 203	91.99 163	73.35 217	67.04 330	83.19 332	56.62 225	92.14 347	59.80 353	69.34 323	87.28 320

VortexMVS		77.62 262	76.44 258	81.13 292	88.58 201	63.73 243	91.24 240	91.30 201	77.81 135	65.76 340	81.97 346	49.69 309	93.72 293	76.40 197	65.26 359	85.94 353

miper_ehance_all_eth		77.60 263	76.44 258	81.09 298	85.70 305	64.41 218	90.65 265	88.64 341	72.31 243	67.37 328	82.52 338	64.77 97	92.64 331	70.67 254	65.30 356	86.24 342

UniMVSNet (Re)		77.58 264	76.78 253	79.98 323	84.11 337	60.80 323	91.76 212	93.17 108	76.56 167	69.93 290	84.78 312	63.32 124	92.36 341	64.89 320	62.51 386	86.78 329

PatchmatchNet		77.46 265	74.63 284	85.96 107	89.55 173	70.35 35	79.97 419	89.55 296	72.23 246	70.94 273	76.91 408	57.03 215	92.79 323	54.27 374	81.17 223	94.74 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v2v482		77.42 266	75.65 273	82.73 244	80.38 382	67.13 134	91.85 205	90.23 267	75.09 186	69.37 292	83.39 329	53.79 262	94.44 258	71.77 241	65.00 363	86.63 334

CHOSEN 280x420		77.35 267	76.95 252	78.55 349	87.07 264	62.68 279	69.71 451	82.95 421	68.80 319	71.48 270	87.27 281	66.03 79	84.00 434	76.47 196	82.81 202	88.95 292

PS-MVSNAJss		77.26 268	76.31 262	80.13 318	80.64 378	59.16 362	90.63 268	91.06 221	72.80 231	68.58 308	84.57 315	53.55 264	93.96 285	72.97 225	71.96 309	87.27 321

gg-mvs-nofinetune		77.18 269	74.31 291	85.80 114	91.42 132	68.36 93	71.78 445	94.72 41	49.61 443	77.12 189	45.92 472	77.41 8	93.98 284	67.62 287	93.16 59	95.05 93

WB-MVSnew		77.14 270	76.18 266	80.01 322	86.18 291	63.24 262	91.26 238	94.11 69	71.72 265	73.52 237	87.29 280	45.14 356	93.00 311	56.98 364	79.42 245	83.80 381

MVP-Stereo		77.12 271	76.23 264	79.79 330	81.72 366	66.34 161	89.29 308	90.88 230	70.56 294	62.01 377	82.88 334	49.34 312	94.13 272	65.55 315	93.80 47	78.88 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
sd_testset		77.08 272	75.37 275	82.20 264	89.25 182	62.11 292	82.06 397	89.09 318	76.77 159	70.84 275	87.12 282	41.43 371	95.01 229	67.23 293	74.55 286	89.48 288

MonoMVSNet		76.99 273	75.08 280	82.73 244	83.32 349	63.24 262	86.47 359	86.37 382	79.08 112	66.31 338	79.30 388	49.80 308	91.72 357	79.37 173	65.70 354	93.23 195

dmvs_re		76.93 274	75.36 276	81.61 281	87.78 246	60.71 330	80.00 418	87.99 360	79.42 101	69.02 298	89.47 238	46.77 339	94.32 262	63.38 329	74.45 289	89.81 281

X-MVStestdata		76.86 275	74.13 297	85.05 147	93.22 70	63.78 239	92.92 142	92.66 132	73.99 201	78.18 174	10.19 487	55.25 239	97.41 81	79.16 176	91.58 83	93.95 168

DU-MVS		76.86 275	75.84 270	79.91 326	82.96 353	60.26 342	91.26 238	91.54 188	76.46 169	68.88 302	86.35 292	56.16 230	92.13 348	66.38 303	62.55 384	87.35 318

Anonymous20240529		76.84 277	74.15 296	84.88 155	91.02 142	64.95 199	93.84 99	91.09 217	53.57 431	73.00 240	87.42 277	35.91 406	97.32 87	69.14 269	72.41 307	92.36 226

UWE-MVS-28		76.83 278	77.60 237	74.51 392	84.58 327	50.34 426	88.22 331	94.60 49	74.46 192	66.66 336	88.98 250	62.53 137	85.50 426	57.55 363	80.80 233	87.69 311

c3_l		76.83 278	75.47 274	80.93 302	85.02 319	64.18 228	90.39 274	88.11 357	71.66 266	66.65 337	81.64 352	63.58 120	92.56 332	69.31 266	62.86 381	86.04 348

WR-MVS		76.76 280	75.74 272	79.82 329	84.60 325	62.27 289	92.60 163	92.51 140	76.06 171	67.87 319	85.34 306	56.76 221	90.24 381	62.20 339	63.69 378	86.94 326

v1144		76.73 281	74.88 281	82.27 260	80.23 386	66.60 155	91.68 219	90.21 270	73.69 211	69.06 297	81.89 347	52.73 274	94.40 260	69.21 267	65.23 360	85.80 356

IterMVS-LS		76.49 282	75.18 279	80.43 310	84.49 330	62.74 277	90.64 266	88.80 334	72.40 241	65.16 346	81.72 350	60.98 155	92.27 345	67.74 285	64.65 368	86.29 340
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V42		76.46 283	74.55 287	82.19 265	79.14 400	67.82 111	90.26 280	89.42 301	73.75 209	68.63 307	81.89 347	51.31 289	94.09 274	71.69 243	64.84 364	84.66 373

Elysia		76.45 284	74.17 294	83.30 228	80.43 380	64.12 229	89.58 296	90.83 232	61.78 390	72.53 250	85.92 299	34.30 413	94.81 236	68.10 279	84.01 190	90.97 265

StellarMVS		76.45 284	74.17 294	83.30 228	80.43 380	64.12 229	89.58 296	90.83 232	61.78 390	72.53 250	85.92 299	34.30 413	94.81 236	68.10 279	84.01 190	90.97 265

mamba_0408		76.22 286	73.37 308	84.77 162	88.50 208	66.98 141	58.80 472	86.18 388	69.12 315	74.12 228	89.01 248	47.50 333	95.35 215	67.57 288	79.52 242	91.98 242

v148		76.19 287	74.47 289	81.36 286	80.05 388	64.44 215	91.75 214	90.23 267	73.68 212	67.13 329	80.84 367	55.92 235	93.86 292	68.95 271	61.73 395	85.76 359

Effi-MVS+-dtu		76.14 288	75.28 278	78.72 348	83.22 350	55.17 401	89.87 291	87.78 364	75.42 180	67.98 314	81.43 356	45.08 357	92.52 334	75.08 207	71.63 310	88.48 301

cl____		76.07 289	74.67 282	80.28 313	85.15 314	61.76 302	90.12 283	88.73 336	71.16 281	65.43 343	81.57 354	61.15 152	92.95 313	66.54 300	62.17 388	86.13 346

DIV-MVS_self_test		76.07 289	74.67 282	80.28 313	85.14 315	61.75 303	90.12 283	88.73 336	71.16 281	65.42 344	81.60 353	61.15 152	92.94 317	66.54 300	62.16 390	86.14 344

FMVSNet2		76.07 289	74.01 299	82.26 262	88.85 194	67.66 115	91.33 235	91.61 186	70.84 288	65.98 339	82.25 342	48.03 323	92.00 352	58.46 358	68.73 331	87.10 323

v144192		76.05 292	74.03 298	82.12 268	79.50 394	66.55 157	91.39 229	89.71 292	72.30 244	68.17 312	81.33 359	51.75 282	94.03 282	67.94 283	64.19 371	85.77 357

NR-MVSNet		76.05 292	74.59 285	80.44 309	82.96 353	62.18 291	90.83 256	91.73 178	77.12 151	60.96 382	86.35 292	59.28 184	91.80 355	60.74 346	61.34 399	87.35 318

v1192		75.98 294	73.92 300	82.15 266	79.73 390	66.24 164	91.22 242	89.75 286	72.67 233	68.49 309	81.42 357	49.86 306	94.27 266	67.08 295	65.02 362	85.95 351

FE-MVS		75.97 295	73.02 314	84.82 158	89.78 166	65.56 182	77.44 430	91.07 220	64.55 357	72.66 246	79.85 382	46.05 349	96.69 134	54.97 371	80.82 231	92.21 236

eth_miper_zixun_eth		75.96 296	74.40 290	80.66 305	84.66 324	63.02 268	89.28 309	88.27 353	71.88 257	65.73 341	81.65 351	59.45 180	92.81 321	68.13 278	60.53 404	86.14 344

TranMVSNet+NR-MVSNet		75.86 297	74.52 288	79.89 327	82.44 359	60.64 333	91.37 232	91.37 195	76.63 165	67.65 321	86.21 295	52.37 277	91.55 363	61.84 341	60.81 402	87.48 314

SCA		75.82 298	72.76 318	85.01 149	86.63 280	70.08 38	81.06 407	89.19 310	71.60 272	70.01 286	77.09 406	45.53 352	90.25 378	60.43 348	73.27 298	94.68 120

LPG-MVS_test		75.82 298	74.58 286	79.56 337	84.31 334	59.37 358	90.44 271	89.73 289	69.49 307	64.86 347	88.42 255	38.65 382	94.30 264	72.56 233	72.76 302	85.01 370

GBi-Net		75.65 300	73.83 301	81.10 295	88.85 194	65.11 194	90.01 287	90.32 258	70.84 288	67.04 330	80.25 377	48.03 323	91.54 364	59.80 353	69.34 323	86.64 331

test1		75.65 300	73.83 301	81.10 295	88.85 194	65.11 194	90.01 287	90.32 258	70.84 288	67.04 330	80.25 377	48.03 323	91.54 364	59.80 353	69.34 323	86.64 331

v1921920		75.63 302	73.49 306	82.06 272	79.38 395	66.35 160	91.07 250	89.48 297	71.98 252	67.99 313	81.22 362	49.16 317	93.90 288	66.56 299	64.56 369	85.92 354

ACMP	71.68 10	75.58 303	74.23 293	79.62 335	84.97 320	59.64 353	90.80 257	89.07 320	70.39 295	62.95 370	87.30 279	38.28 386	93.87 290	72.89 226	71.45 313	85.36 366
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v8		75.35 304	73.26 312	81.61 281	80.67 377	66.82 147	89.54 300	89.27 306	71.65 267	63.30 365	80.30 376	54.99 245	94.06 277	67.33 292	62.33 387	83.94 379

tpm cat1		75.30 305	72.21 327	84.58 178	88.52 207	67.77 112	78.16 428	88.02 359	61.88 388	68.45 310	76.37 413	60.65 160	94.03 282	53.77 378	74.11 292	91.93 245

PLC	68.80 14	75.23 306	73.68 304	79.86 328	92.93 81	58.68 367	90.64 266	88.30 351	60.90 395	64.43 355	90.53 209	42.38 367	94.57 250	56.52 365	76.54 277	86.33 339
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1240		75.21 307	72.98 316	81.88 274	79.20 397	66.00 169	90.75 260	89.11 317	71.63 271	67.41 326	81.22 362	47.36 335	93.87 290	65.46 316	64.72 367	85.77 357

blend_shiyan4		75.18 308	73.00 315	81.69 279	75.62 432	64.75 202	91.78 209	91.06 221	65.89 348	61.35 379	77.39 400	62.16 142	93.71 294	68.18 276	63.60 379	86.61 336

Fast-Effi-MVS+-dtu		75.04 309	73.37 308	80.07 319	80.86 372	59.52 356	91.20 244	85.38 398	71.90 255	65.20 345	84.84 311	41.46 370	92.97 312	66.50 302	72.96 301	87.73 310

dp		75.01 310	72.09 328	83.76 208	89.28 181	66.22 165	79.96 420	89.75 286	71.16 281	67.80 320	77.19 405	51.81 280	92.54 333	50.39 388	71.44 314	92.51 223

TAPA-MVS	70.22 12	74.94 311	73.53 305	79.17 343	90.40 155	52.07 414	89.19 313	89.61 295	62.69 379	70.07 285	92.67 151	48.89 320	94.32 262	38.26 444	79.97 238	91.12 263
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SSC-MVS3.2		74.92 312	73.32 311	79.74 332	86.53 283	60.31 341	89.03 318	92.70 127	78.61 123	68.98 300	83.34 330	41.93 369	92.23 346	52.77 382	65.97 352	86.69 330

SSM_04072		74.86 313	73.37 308	79.35 340	88.50 208	66.98 141	58.80 472	86.18 388	69.12 315	74.12 228	89.01 248	47.50 333	79.09 456	67.57 288	79.52 242	91.98 242

v10		74.77 314	72.54 324	81.46 284	80.33 384	66.71 152	89.15 314	89.08 319	70.94 286	63.08 368	79.86 381	52.52 275	94.04 280	65.70 312	62.17 388	83.64 382

XVG-OURS-SEG-HR		74.70 315	73.08 313	79.57 336	78.25 413	57.33 384	80.49 410	87.32 369	63.22 372	68.76 305	90.12 230	44.89 358	91.59 361	70.55 256	74.09 293	89.79 282

ACMM	69.62 13	74.34 316	72.73 320	79.17 343	84.25 336	57.87 374	90.36 276	89.93 280	63.17 374	65.64 342	86.04 298	37.79 394	94.10 273	65.89 308	71.52 312	85.55 362
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		74.31 317	72.30 326	80.32 311	91.49 131	61.66 305	90.85 255	80.72 427	56.67 422	63.85 360	90.64 206	46.75 340	90.84 372	53.79 377	75.99 281	88.47 302

XVG-OURS		74.25 318	72.46 325	79.63 334	78.45 411	57.59 380	80.33 412	87.39 366	63.86 364	68.76 305	89.62 237	40.50 375	91.72 357	69.00 270	74.25 291	89.58 285

test_fmvs1		74.07 319	73.69 303	75.22 382	78.91 404	47.34 443	89.06 317	74.69 444	63.68 367	79.41 157	91.59 188	24.36 446	87.77 408	85.22 97	76.26 279	90.55 273

CVMVSNet		74.04 320	74.27 292	73.33 402	85.33 308	43.94 457	89.53 303	88.39 347	54.33 430	70.37 281	90.13 228	49.17 316	84.05 432	61.83 342	79.36 247	91.99 241

Baseline_NR-MVSNet		73.99 321	72.83 317	77.48 361	80.78 375	59.29 361	91.79 207	84.55 407	68.85 318	68.99 299	80.70 368	56.16 230	92.04 351	62.67 336	60.98 401	81.11 414

pmmvs4		73.92 322	71.81 332	80.25 315	79.17 398	65.24 190	87.43 346	87.26 372	67.64 335	63.46 363	83.91 324	48.96 319	91.53 367	62.94 333	65.49 355	83.96 378

D2MVS		73.80 323	72.02 329	79.15 345	79.15 399	62.97 269	88.58 325	90.07 273	72.94 226	59.22 393	78.30 392	42.31 368	92.70 327	65.59 314	72.00 308	81.79 409

SD_0403		73.79 324	73.48 307	74.69 389	85.33 308	45.56 453	83.80 376	85.57 397	76.55 168	62.96 369	88.45 254	50.62 298	87.59 412	48.80 398	79.28 251	90.92 267

CR-MVSNet		73.79 324	70.82 340	82.70 246	83.15 351	67.96 106	70.25 448	84.00 412	73.67 213	69.97 288	72.41 430	57.82 208	89.48 391	52.99 381	73.13 299	90.64 271

test_djsdf		73.76 326	72.56 323	77.39 363	77.00 425	53.93 407	89.07 315	90.69 241	65.80 349	63.92 358	82.03 345	43.14 365	92.67 328	72.83 227	68.53 332	85.57 361

pmmvs5		73.35 327	71.52 334	78.86 347	78.64 408	60.61 334	91.08 248	86.90 376	67.69 332	63.32 364	83.64 325	44.33 360	90.53 375	62.04 340	66.02 351	85.46 364

Anonymous20231211		73.08 328	70.39 344	81.13 292	90.62 150	63.33 259	91.40 227	90.06 275	51.84 436	64.46 354	80.67 370	36.49 404	94.07 276	63.83 326	64.17 372	85.98 350

tt0805		73.07 329	70.73 341	80.07 319	78.37 412	57.05 387	87.78 340	92.18 154	61.23 394	67.04 330	86.49 291	31.35 427	94.58 248	65.06 319	67.12 344	88.57 299

miper_lstm_enhance		73.05 330	71.73 333	77.03 368	83.80 342	58.32 371	81.76 398	88.88 330	69.80 305	61.01 381	78.23 394	57.19 213	87.51 414	65.34 317	59.53 409	85.27 369

jajsoiax		73.05 330	71.51 335	77.67 358	77.46 422	54.83 403	88.81 321	90.04 276	69.13 314	62.85 372	83.51 327	31.16 428	92.75 324	70.83 251	69.80 319	85.43 365

LCM-MVSNet-Re		72.93 332	71.84 331	76.18 377	88.49 212	48.02 438	80.07 417	70.17 459	73.96 204	52.25 427	80.09 380	49.98 304	88.24 402	67.35 290	84.23 185	92.28 231

pm-mvs1		72.89 333	71.09 337	78.26 353	79.10 401	57.62 378	90.80 257	89.30 305	67.66 333	62.91 371	81.78 349	49.11 318	92.95 313	60.29 350	58.89 412	84.22 377

tpmvs		72.88 334	69.76 350	82.22 263	90.98 143	67.05 136	78.22 427	88.30 351	63.10 375	64.35 356	74.98 420	55.09 244	94.27 266	43.25 424	69.57 322	85.34 367

test0.0.03 1		72.76 335	72.71 321	72.88 406	80.25 385	47.99 439	91.22 242	89.45 299	71.51 276	62.51 375	87.66 272	53.83 260	85.06 428	50.16 390	67.84 342	85.58 360

UniMVSNet_ETH3D		72.74 336	70.53 343	79.36 339	78.62 409	56.64 391	85.01 366	89.20 309	63.77 365	64.84 349	84.44 317	34.05 415	91.86 354	63.94 325	70.89 317	89.57 286

mvs_tets		72.71 337	71.11 336	77.52 359	77.41 423	54.52 405	88.45 327	89.76 285	68.76 321	62.70 373	83.26 331	29.49 434	92.71 325	70.51 257	69.62 321	85.34 367

FMVSNet1		72.71 337	69.91 348	81.10 295	83.60 346	65.11 194	90.01 287	90.32 258	63.92 363	63.56 362	80.25 377	36.35 405	91.54 364	54.46 373	66.75 347	86.64 331

test_fmvs1_n		72.69 339	71.92 330	74.99 387	71.15 447	47.08 445	87.34 348	75.67 439	63.48 369	78.08 176	91.17 201	20.16 460	87.87 405	84.65 106	75.57 283	90.01 279

IterMVS		72.65 340	70.83 338	78.09 355	82.17 361	62.96 270	87.64 344	86.28 384	71.56 274	60.44 386	78.85 390	45.42 354	86.66 418	63.30 331	61.83 392	84.65 374
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
myMVS_eth3d		72.58 341	72.74 319	72.10 414	87.87 241	49.45 432	88.07 333	89.01 324	72.91 228	63.11 366	88.10 264	63.63 115	85.54 423	32.73 460	69.23 326	81.32 412

PatchMatch-RL		72.06 342	69.98 345	78.28 352	89.51 174	55.70 398	83.49 379	83.39 419	61.24 393	63.72 361	82.76 335	34.77 410	93.03 310	53.37 380	77.59 264	86.12 347

PVSNet_0	68.08 15	71.81 343	68.32 359	82.27 260	84.68 322	62.31 288	88.68 323	90.31 261	75.84 173	57.93 405	80.65 371	37.85 393	94.19 269	69.94 259	29.05 475	90.31 275

MIMVSNet		71.64 344	68.44 357	81.23 289	81.97 364	64.44 215	73.05 442	88.80 334	69.67 306	64.59 350	74.79 422	32.79 419	87.82 406	53.99 375	76.35 278	91.42 253

test_vis1_n		71.63 345	70.73 341	74.31 396	69.63 454	47.29 444	86.91 352	72.11 452	63.21 373	75.18 211	90.17 225	20.40 458	85.76 422	84.59 107	74.42 290	89.87 280

IterMVS-SCA-FT		71.55 346	69.97 346	76.32 375	81.48 368	60.67 332	87.64 344	85.99 391	66.17 346	59.50 391	78.88 389	45.53 352	83.65 436	62.58 337	61.93 391	84.63 376

v7n		71.31 347	68.65 354	79.28 341	76.40 427	60.77 325	86.71 356	89.45 299	64.17 362	58.77 398	78.24 393	44.59 359	93.54 299	57.76 360	61.75 394	83.52 385

anonymousdsp		71.14 348	69.37 352	76.45 374	72.95 442	54.71 404	84.19 373	88.88 330	61.92 387	62.15 376	79.77 383	38.14 389	91.44 369	68.90 272	67.45 343	83.21 391

usedtu_blend_shiyan5		71.06 349	67.54 362	81.62 280	75.39 433	64.75 202	85.67 362	86.47 381	56.48 423	60.64 384	76.85 409	47.20 337	93.71 294	68.18 276	50.98 435	86.40 338

F-COLMAP		70.66 350	68.44 357	77.32 364	86.37 288	55.91 396	88.00 335	86.32 383	56.94 420	57.28 409	88.07 266	33.58 417	92.49 335	51.02 385	68.37 333	83.55 383

WR-MVS_H		70.59 351	69.94 347	72.53 408	81.03 371	51.43 418	87.35 347	92.03 162	67.38 336	60.23 388	80.70 368	55.84 236	83.45 439	46.33 413	58.58 414	82.72 398

CP-MVSNet		70.50 352	69.91 348	72.26 411	80.71 376	51.00 422	87.23 349	90.30 262	67.84 331	59.64 390	82.69 336	50.23 302	82.30 447	51.28 384	59.28 410	83.46 387

RPMNet		70.42 353	65.68 374	84.63 176	83.15 351	67.96 106	70.25 448	90.45 249	46.83 452	69.97 288	65.10 455	56.48 229	95.30 221	35.79 449	73.13 299	90.64 271

testing3		70.38 354	70.83 338	69.03 427	85.82 302	43.93 458	90.72 263	90.56 248	68.06 327	60.24 387	86.82 288	64.83 95	84.12 430	26.33 468	64.10 373	79.04 434

tfpnnormal		70.10 355	67.36 364	78.32 351	83.45 348	60.97 321	88.85 319	92.77 125	64.85 356	60.83 383	78.53 391	43.52 363	93.48 301	31.73 463	61.70 396	80.52 421

TransMVSNet (Re)		70.07 356	67.66 361	77.31 365	80.62 379	59.13 363	91.78 209	84.94 403	65.97 347	60.08 389	80.44 373	50.78 295	91.87 353	48.84 397	45.46 448	80.94 416

CL-MVSNet_self_test		69.92 357	68.09 360	75.41 380	73.25 441	55.90 397	90.05 286	89.90 281	69.96 302	61.96 378	76.54 410	51.05 294	87.64 409	49.51 394	50.59 437	82.70 400

DP-MVS		69.90 358	66.48 366	80.14 317	95.36 29	62.93 271	89.56 298	76.11 437	50.27 442	57.69 407	85.23 307	39.68 378	95.73 190	33.35 454	71.05 316	81.78 410

PS-CasMVS		69.86 359	69.13 353	72.07 415	80.35 383	50.57 425	87.02 351	89.75 286	67.27 337	59.19 394	82.28 341	46.58 342	82.24 448	50.69 387	59.02 411	83.39 389

Syy-MVS		69.65 360	69.52 351	70.03 423	87.87 241	43.21 459	88.07 333	89.01 324	72.91 228	63.11 366	88.10 264	45.28 355	85.54 423	22.07 473	69.23 326	81.32 412

MSDG		69.54 361	65.73 373	80.96 300	85.11 317	63.71 245	84.19 373	83.28 420	56.95 419	54.50 416	84.03 321	31.50 425	96.03 169	42.87 428	69.13 328	83.14 393

PEN-MVS		69.46 362	68.56 355	72.17 413	79.27 396	49.71 430	86.90 353	89.24 307	67.24 340	59.08 395	82.51 339	47.23 336	83.54 438	48.42 400	57.12 416	83.25 390

LS3D		69.17 363	66.40 368	77.50 360	91.92 116	56.12 394	85.12 365	80.37 429	46.96 450	56.50 411	87.51 276	37.25 397	93.71 294	32.52 462	79.40 246	82.68 401

PatchT		69.11 364	65.37 378	80.32 311	82.07 363	63.68 249	67.96 458	87.62 365	50.86 440	69.37 292	65.18 454	57.09 214	88.53 398	41.59 433	66.60 348	88.74 296

KD-MVS_2432*1600		69.03 365	66.37 369	77.01 369	85.56 306	61.06 319	81.44 403	90.25 265	67.27 337	58.00 403	76.53 411	54.49 250	87.63 410	48.04 402	35.77 466	82.34 404

miper_refine_blended		69.03 365	66.37 369	77.01 369	85.56 306	61.06 319	81.44 403	90.25 265	67.27 337	58.00 403	76.53 411	54.49 250	87.63 410	48.04 402	35.77 466	82.34 404

mvsany_test1		68.77 367	68.56 355	69.39 425	73.57 440	45.88 452	80.93 408	60.88 473	59.65 404	71.56 268	90.26 218	43.22 364	75.05 460	74.26 216	62.70 383	87.25 322

ACMH	63.93 17	68.62 368	64.81 380	80.03 321	85.22 313	63.25 261	87.72 341	84.66 405	60.83 396	51.57 431	79.43 387	27.29 441	94.96 231	41.76 431	64.84 364	81.88 408
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EG-PatchMatch MVS		68.55 369	65.41 377	77.96 356	78.69 407	62.93 271	89.86 292	89.17 311	60.55 397	50.27 437	77.73 399	22.60 454	94.06 277	47.18 409	72.65 304	76.88 448

ADS-MVSNet		68.54 370	64.38 387	81.03 299	88.06 232	66.90 146	68.01 456	84.02 411	57.57 413	64.48 352	69.87 442	38.68 380	89.21 393	40.87 435	67.89 340	86.97 324

DTE-MVSNet		68.46 371	67.33 365	71.87 417	77.94 417	49.00 436	86.16 361	88.58 343	66.36 345	58.19 400	82.21 343	46.36 343	83.87 435	44.97 421	55.17 423	82.73 397

mmtdpeth		68.33 372	66.37 369	74.21 397	82.81 356	51.73 415	84.34 371	80.42 428	67.01 341	71.56 268	68.58 446	30.52 432	92.35 342	75.89 200	36.21 464	78.56 441

our_test_3		68.29 373	64.69 382	79.11 346	78.92 402	64.85 201	88.40 328	85.06 401	60.32 400	52.68 425	76.12 415	40.81 374	89.80 390	44.25 423	55.65 421	82.67 402

Patchmatch-RL test		68.17 374	64.49 385	79.19 342	71.22 446	53.93 407	70.07 450	71.54 456	69.22 311	56.79 410	62.89 459	56.58 226	88.61 395	69.53 263	52.61 431	95.03 95

XVG-ACMP-BASELINE		68.04 375	65.53 376	75.56 379	74.06 439	52.37 412	78.43 424	85.88 392	62.03 385	58.91 397	81.21 364	20.38 459	91.15 371	60.69 347	68.18 334	83.16 392

FMVSNet5		68.04 375	65.66 375	75.18 384	84.43 332	57.89 373	83.54 378	86.26 385	61.83 389	53.64 422	73.30 425	37.15 400	85.08 427	48.99 396	61.77 393	82.56 403

ppachtmachnet_test		67.72 377	63.70 390	79.77 331	78.92 402	66.04 168	88.68 323	82.90 422	60.11 402	55.45 413	75.96 416	39.19 379	90.55 374	39.53 439	52.55 432	82.71 399

ACMH+	65.35 16	67.65 378	64.55 383	76.96 371	84.59 326	57.10 386	88.08 332	80.79 426	58.59 411	53.00 424	81.09 366	26.63 443	92.95 313	46.51 411	61.69 397	80.82 417

pmmvs6		67.57 379	64.76 381	76.00 378	72.82 444	53.37 409	88.71 322	86.78 380	53.19 432	57.58 408	78.03 396	35.33 409	92.41 338	55.56 369	54.88 425	82.21 406

Anonymous20231206		67.53 380	65.78 372	72.79 407	74.95 435	47.59 441	88.23 330	87.32 369	61.75 392	58.07 402	77.29 403	37.79 394	87.29 416	42.91 426	63.71 377	83.48 386

Patchmtry		67.53 380	63.93 389	78.34 350	82.12 362	64.38 219	68.72 453	84.00 412	48.23 449	59.24 392	72.41 430	57.82 208	89.27 392	46.10 414	56.68 420	81.36 411

USDC		67.43 382	64.51 384	76.19 376	77.94 417	55.29 400	78.38 425	85.00 402	73.17 219	48.36 445	80.37 374	21.23 456	92.48 336	52.15 383	64.02 375	80.81 418

ADS-MVSNet2		66.90 383	63.44 392	77.26 366	88.06 232	60.70 331	68.01 456	75.56 441	57.57 413	64.48 352	69.87 442	38.68 380	84.10 431	40.87 435	67.89 340	86.97 324

FE-MVSNET2		66.80 384	64.06 388	75.03 385	69.84 452	57.11 385	86.57 357	88.57 344	67.94 330	50.97 435	72.16 434	33.79 416	87.55 413	53.94 376	52.74 429	80.45 422

CMPMVS	48.56 21	66.77 385	64.41 386	73.84 399	70.65 450	50.31 427	77.79 429	85.73 395	45.54 455	44.76 456	82.14 344	35.40 408	90.14 384	63.18 332	74.54 288	81.07 415
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	61.12 18	66.39 386	62.92 395	76.80 373	76.51 426	57.77 375	89.22 310	83.41 418	55.48 427	53.86 420	77.84 397	26.28 444	93.95 286	34.90 451	68.76 330	78.68 439

LTVRE_ROB	59.60 19	66.27 387	63.54 391	74.45 393	84.00 339	51.55 417	67.08 460	83.53 416	58.78 409	54.94 415	80.31 375	34.54 411	93.23 306	40.64 437	68.03 336	78.58 440
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		66.06 388	62.45 398	76.88 372	81.42 370	54.45 406	57.49 474	88.67 339	49.36 444	63.86 359	46.86 471	56.06 233	90.25 378	49.53 393	68.83 329	85.95 351

Patchmatch-test		65.86 389	60.94 404	80.62 308	83.75 343	58.83 365	58.91 471	75.26 443	44.50 458	50.95 436	77.09 406	58.81 193	87.90 404	35.13 450	64.03 374	95.12 89

UnsupCasMVSNet_eth		65.79 390	63.10 393	73.88 398	70.71 449	50.29 428	81.09 406	89.88 282	72.58 235	49.25 442	74.77 423	32.57 421	87.43 415	55.96 368	41.04 456	83.90 380

test_fmvs2		65.78 391	64.84 379	68.60 429	66.54 461	41.71 461	83.27 383	69.81 460	54.38 429	67.91 316	84.54 316	15.35 465	81.22 452	75.65 202	66.16 350	82.88 394

dmvs_testset		65.55 392	66.45 367	62.86 441	79.87 389	22.35 487	76.55 432	71.74 454	77.42 148	55.85 412	87.77 271	51.39 288	80.69 453	31.51 466	65.92 353	85.55 362

pmmvs-eth3d		65.53 393	62.32 399	75.19 383	69.39 455	59.59 354	82.80 391	83.43 417	62.52 380	51.30 433	72.49 428	32.86 418	87.16 417	55.32 370	50.73 436	78.83 437

mamv4		65.18 394	67.43 363	58.44 445	77.88 419	49.36 435	69.40 452	70.99 458	48.31 448	57.78 406	85.53 304	59.01 190	51.88 483	73.67 218	64.32 370	74.07 453

SixPastTwentyTwo		64.92 395	61.78 402	74.34 395	78.74 406	49.76 429	83.42 382	79.51 432	62.86 376	50.27 437	77.35 401	30.92 430	90.49 376	45.89 415	47.06 443	82.78 395

OurMVSNet-221017-0		64.68 396	62.17 400	72.21 412	76.08 430	47.35 442	80.67 409	81.02 425	56.19 424	51.60 430	79.66 385	27.05 442	88.56 397	53.60 379	53.63 428	80.71 419

test_0402		64.54 397	61.09 403	74.92 388	84.10 338	60.75 327	87.95 336	79.71 431	52.03 434	52.41 426	77.20 404	32.21 423	91.64 359	23.14 471	61.03 400	72.36 459

testgi		64.48 398	62.87 396	69.31 426	71.24 445	40.62 464	85.49 363	79.92 430	65.36 353	54.18 418	83.49 328	23.74 449	84.55 429	41.60 432	60.79 403	82.77 396

RPSCF		64.24 399	61.98 401	71.01 420	76.10 429	45.00 454	75.83 437	75.94 438	46.94 451	58.96 396	84.59 314	31.40 426	82.00 449	47.76 407	60.33 408	86.04 348

EU-MVSNet		64.01 400	63.01 394	67.02 435	74.40 438	38.86 470	83.27 383	86.19 387	45.11 456	54.27 417	81.15 365	36.91 403	80.01 455	48.79 399	57.02 417	82.19 407

test20.03		63.83 401	62.65 397	67.38 434	70.58 451	39.94 466	86.57 357	84.17 409	63.29 371	51.86 429	77.30 402	37.09 401	82.47 445	38.87 443	54.13 427	79.73 428

sc_t1		63.81 402	59.39 410	77.10 367	77.62 420	56.03 395	84.32 372	73.56 448	46.66 453	58.22 399	73.06 426	23.28 452	90.62 373	50.93 386	46.84 444	84.64 375

MDA-MVSNet_test_wron		63.78 403	60.16 406	74.64 390	78.15 415	60.41 338	83.49 379	84.03 410	56.17 426	39.17 466	71.59 437	37.22 398	83.24 442	42.87 428	48.73 439	80.26 425

YYNet1		63.76 404	60.14 407	74.62 391	78.06 416	60.19 345	83.46 381	83.99 414	56.18 425	39.25 465	71.56 438	37.18 399	83.34 440	42.90 427	48.70 440	80.32 424

K. test v3		63.09 405	59.61 409	73.53 401	76.26 428	49.38 434	83.27 383	77.15 435	64.35 359	47.77 447	72.32 432	28.73 436	87.79 407	49.93 392	36.69 463	83.41 388

COLMAP_ROB	57.96 20	62.98 406	59.65 408	72.98 405	81.44 369	53.00 411	83.75 377	75.53 442	48.34 447	48.81 444	81.40 358	24.14 447	90.30 377	32.95 457	60.52 405	75.65 451
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20240521		62.09 407	59.08 411	71.10 419	67.19 459	48.72 437	83.91 375	85.23 400	50.38 441	47.84 446	71.22 440	20.74 457	85.51 425	46.47 412	58.75 413	79.06 433

tt0320		61.85 408	57.45 417	75.03 385	77.49 421	57.60 379	82.74 392	73.65 447	43.65 462	53.65 421	68.18 448	25.47 445	88.66 394	45.56 417	46.68 445	78.81 438

AllTest		61.66 409	58.06 413	72.46 409	79.57 391	51.42 419	80.17 415	68.61 462	51.25 438	45.88 450	81.23 360	19.86 461	86.58 419	38.98 441	57.01 418	79.39 430

UnsupCasMVSNet_bld		61.60 410	57.71 414	73.29 403	68.73 456	51.64 416	78.61 423	89.05 322	57.20 418	46.11 449	61.96 462	28.70 437	88.60 396	50.08 391	38.90 461	79.63 429

MDA-MVSNet-bldmvs		61.54 411	57.70 415	73.05 404	79.53 393	57.00 390	83.08 387	81.23 424	57.57 413	34.91 470	72.45 429	32.79 419	86.26 421	35.81 448	41.95 454	75.89 450

tt0320-xc		61.51 412	56.89 421	75.37 381	78.50 410	58.61 368	82.61 394	71.27 457	44.31 459	53.17 423	68.03 450	23.38 450	88.46 399	47.77 406	43.00 453	79.03 435

mvs5depth		61.03 413	57.65 416	71.18 418	67.16 460	47.04 447	72.74 443	77.49 433	57.47 416	60.52 385	72.53 427	22.84 453	88.38 400	49.15 395	38.94 460	78.11 444

KD-MVS_self_test		60.87 414	58.60 412	67.68 432	66.13 462	39.93 467	75.63 439	84.70 404	57.32 417	49.57 440	68.45 447	29.55 433	82.87 443	48.09 401	47.94 441	80.25 426

kuosan		60.86 415	60.24 405	62.71 442	81.57 367	46.43 449	75.70 438	85.88 392	57.98 412	48.95 443	69.53 444	58.42 198	76.53 458	28.25 467	35.87 465	65.15 466

FE-MVSNET		60.52 416	57.18 420	70.53 421	67.53 458	50.68 424	82.62 393	76.28 436	59.33 407	46.71 448	71.10 441	30.54 431	83.61 437	33.15 456	47.37 442	77.29 447

TinyColmap		60.32 417	56.42 424	72.00 416	78.78 405	53.18 410	78.36 426	75.64 440	52.30 433	41.59 464	75.82 418	14.76 468	88.35 401	35.84 447	54.71 426	74.46 452

MVS-HIRNet		60.25 418	55.55 425	74.35 394	84.37 333	56.57 392	71.64 446	74.11 445	34.44 469	45.54 454	42.24 477	31.11 429	89.81 388	40.36 438	76.10 280	76.67 449

MIMVSNet1		60.16 419	57.33 418	68.67 428	69.71 453	44.13 456	78.92 422	84.21 408	55.05 428	44.63 457	71.85 435	23.91 448	81.54 451	32.63 461	55.03 424	80.35 423

PM-MVS		59.40 420	56.59 422	67.84 430	63.63 465	41.86 460	76.76 431	63.22 470	59.01 408	51.07 434	72.27 433	11.72 472	83.25 441	61.34 343	50.28 438	78.39 442

new-patchmatchnet		59.30 421	56.48 423	67.79 431	65.86 463	44.19 455	82.47 395	81.77 423	59.94 403	43.65 460	66.20 453	27.67 440	81.68 450	39.34 440	41.40 455	77.50 446

test_vis1_rt		59.09 422	57.31 419	64.43 438	68.44 457	46.02 451	83.05 389	48.63 482	51.96 435	49.57 440	63.86 458	16.30 463	80.20 454	71.21 249	62.79 382	67.07 465

test_fmvs3		56.82 423	54.86 427	62.69 443	53.59 476	35.47 473	75.87 436	65.64 467	43.91 460	55.10 414	71.43 439	6.91 480	74.40 463	68.64 274	52.63 430	78.20 443

DSMNet-mixed		56.78 424	54.44 428	63.79 439	63.21 466	29.44 482	64.43 463	64.10 469	42.12 466	51.32 432	71.60 436	31.76 424	75.04 461	36.23 446	65.20 361	86.87 328

pmmvs3		55.51 425	51.50 431	67.53 433	57.90 474	50.93 423	80.37 411	73.66 446	40.63 467	44.15 459	64.75 456	16.30 463	78.97 457	44.77 422	40.98 458	72.69 457

TDRefinement		55.28 426	51.58 430	66.39 436	59.53 473	46.15 450	76.23 434	72.80 449	44.60 457	42.49 462	76.28 414	15.29 466	82.39 446	33.20 455	43.75 450	70.62 461

dongtai		55.18 427	55.46 426	54.34 453	76.03 431	36.88 471	76.07 435	84.61 406	51.28 437	43.41 461	64.61 457	56.56 227	67.81 471	18.09 476	28.50 476	58.32 469

LF4IMVS		54.01 428	52.12 429	59.69 444	62.41 468	39.91 468	68.59 454	68.28 464	42.96 464	44.55 458	75.18 419	14.09 470	68.39 470	41.36 434	51.68 433	70.78 460

ttmdpeth		53.34 429	49.96 432	63.45 440	62.07 470	40.04 465	72.06 444	65.64 467	42.54 465	51.88 428	77.79 398	13.94 471	76.48 459	32.93 458	30.82 474	73.84 454

MVStest1		51.35 430	46.89 434	64.74 437	65.06 464	51.10 421	67.33 459	72.58 450	30.20 473	35.30 468	74.82 421	27.70 439	69.89 468	24.44 470	24.57 477	73.22 455

N_pmnet		50.55 431	49.11 433	54.88 451	77.17 424	4.02 495	84.36 370	2.00 493	48.59 445	45.86 452	68.82 445	32.22 422	82.80 444	31.58 464	51.38 434	77.81 445

new_pmnet		49.31 432	46.44 435	57.93 446	62.84 467	40.74 463	68.47 455	62.96 471	36.48 468	35.09 469	57.81 466	14.97 467	72.18 465	32.86 459	46.44 446	60.88 468

mvsany_test3		48.86 433	46.35 436	56.41 447	46.00 482	31.67 478	62.26 465	47.25 483	43.71 461	45.54 454	68.15 449	10.84 473	64.44 479	57.95 359	35.44 468	73.13 456

test_f		46.58 434	43.45 438	55.96 448	45.18 483	32.05 477	61.18 466	49.49 481	33.39 470	42.05 463	62.48 461	7.00 479	65.56 475	47.08 410	43.21 452	70.27 462

WB-MVS		46.23 435	44.94 437	50.11 456	62.13 469	21.23 489	76.48 433	55.49 475	45.89 454	35.78 467	61.44 464	35.54 407	72.83 464	9.96 483	21.75 478	56.27 471

FPMVS		45.64 436	43.10 440	53.23 454	51.42 479	36.46 472	64.97 462	71.91 453	29.13 474	27.53 474	61.55 463	9.83 475	65.01 477	16.00 480	55.58 422	58.22 470

SSC-MVS		44.51 437	43.35 439	47.99 460	61.01 472	18.90 491	74.12 441	54.36 476	43.42 463	34.10 471	60.02 465	34.42 412	70.39 467	9.14 485	19.57 479	54.68 472

EGC-MVSNET		42.35 438	38.09 441	55.11 450	74.57 436	46.62 448	71.63 447	55.77 474	0.04 488	0.24 489	62.70 460	14.24 469	74.91 462	17.59 477	46.06 447	43.80 474

LCM-MVSNet		40.54 439	35.79 444	54.76 452	36.92 489	30.81 479	51.41 477	69.02 461	22.07 476	24.63 476	45.37 473	4.56 484	65.81 474	33.67 453	34.50 469	67.67 463

APD_test1		40.50 440	37.31 443	50.09 457	51.88 477	35.27 474	59.45 470	52.59 478	21.64 477	26.12 475	57.80 467	4.56 484	66.56 473	22.64 472	39.09 459	48.43 473

test_vis3_rt		40.46 441	37.79 442	48.47 459	44.49 484	33.35 476	66.56 461	32.84 490	32.39 471	29.65 472	39.13 480	3.91 487	68.65 469	50.17 389	40.99 457	43.40 475

ANet_high		40.27 442	35.20 445	55.47 449	34.74 490	34.47 475	63.84 464	71.56 455	48.42 446	18.80 479	41.08 478	9.52 476	64.45 478	20.18 474	8.66 486	67.49 464

test_method		38.59 443	35.16 446	48.89 458	54.33 475	21.35 488	45.32 480	53.71 477	7.41 485	28.74 473	51.62 469	8.70 477	52.87 482	33.73 452	32.89 470	72.47 458

PMMVS2		37.93 444	33.61 447	50.92 455	46.31 481	24.76 485	60.55 469	50.05 479	28.94 475	20.93 477	47.59 470	4.41 486	65.13 476	25.14 469	18.55 481	62.87 467

Gipuma		34.91 445	31.44 448	45.30 461	70.99 448	39.64 469	19.85 484	72.56 451	20.10 479	16.16 483	21.47 484	5.08 483	71.16 466	13.07 481	43.70 451	25.08 481
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testf1		32.77 446	29.47 449	42.67 463	41.89 486	30.81 479	52.07 475	43.45 484	15.45 480	18.52 480	44.82 474	2.12 488	58.38 480	16.05 478	30.87 472	38.83 476

APD_test2		32.77 446	29.47 449	42.67 463	41.89 486	30.81 479	52.07 475	43.45 484	15.45 480	18.52 480	44.82 474	2.12 488	58.38 480	16.05 478	30.87 472	38.83 476

PMVS	26.43 22	31.84 448	28.16 451	42.89 462	25.87 492	27.58 483	50.92 478	49.78 480	21.37 478	14.17 484	40.81 479	2.01 490	66.62 472	9.61 484	38.88 462	34.49 480
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		24.61 449	24.00 453	26.45 467	43.74 485	18.44 492	60.86 467	39.66 486	15.11 482	9.53 486	22.10 483	6.52 481	46.94 485	8.31 486	10.14 483	13.98 483

MVE	24.84 23	24.35 450	19.77 456	38.09 465	34.56 491	26.92 484	26.57 482	38.87 488	11.73 484	11.37 485	27.44 481	1.37 491	50.42 484	11.41 482	14.60 482	36.93 478
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		23.76 451	23.20 455	25.46 468	41.52 488	16.90 493	60.56 468	38.79 489	14.62 483	8.99 487	20.24 486	7.35 478	45.82 486	7.25 487	9.46 484	13.64 484

tmp_tt		22.26 452	23.75 454	17.80 469	5.23 493	12.06 494	35.26 481	39.48 487	2.82 487	18.94 478	44.20 476	22.23 455	24.64 488	36.30 445	9.31 485	16.69 482

cdsmvs_eth3d_5k		19.86 453	26.47 452	0.00 473	0.00 496	0.00 498	0.00 485	93.45 95	0.00 491	0.00 492	95.27 77	49.56 310	0.00 492	0.00 491	0.00 489	0.00 488

wuyk23d		11.30 454	10.95 457	12.33 470	48.05 480	19.89 490	25.89 483	1.92 494	3.58 486	3.12 488	1.37 488	0.64 492	15.77 489	6.23 488	7.77 487	1.35 485

ab-mvs-re		7.91 455	10.55 458	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	94.95 87	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

testmvs		7.23 456	9.62 459	0.06 472	0.04 494	0.02 497	84.98 367	0.02 495	0.03 489	0.18 490	1.21 489	0.01 494	0.02 490	0.14 489	0.01 488	0.13 487

test123		6.92 457	9.21 460	0.08 471	0.03 495	0.05 496	81.65 401	0.01 496	0.02 490	0.14 491	0.85 490	0.03 493	0.02 490	0.12 490	0.00 489	0.16 486

pcd_1.5k_mvsjas		4.46 458	5.95 461	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	53.55 264	0.00 492	0.00 491	0.00 489	0.00 488

mmdepth		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

monomultidepth		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

test_blank		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

uanet_test		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

DCPMVS		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

sosnet-low-res		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

sosnet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

uncertanet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

Regformer		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

uanet		0.00 459	0.00 462	0.00 473	0.00 496	0.00 498	0.00 485	0.00 497	0.00 491	0.00 492	0.00 491	0.00 495	0.00 492	0.00 491	0.00 489	0.00 488

MED-MVS test				87.42 45	94.76 34	67.28 125	94.47 61	94.87 32	73.09 224	91.27 23	96.95 17		98.98 16	91.55 43	94.28 37	95.99 45

TestfortrainingZip							94.47 61

WAC-MVS						49.45 432								31.56 465

FOURS1					93.95 50	61.77 301	93.96 88	91.92 166	62.14 384	86.57 62

MSC_two_6792asdad				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 38	94.77 26	96.51 24

PC_three_1452									80.91 65	94.07 2	96.83 29	83.57 4	99.12 5	95.70 10	97.42 4	97.55 4

No_MVS				89.60 9	97.31 4	73.22 12		95.05 29					99.07 13	92.01 38	94.77 26	96.51 24

test_one_0601					96.32 19	69.74 51		94.18 66	71.42 278	90.67 29	96.85 27	74.45 22

eth-test2					0.00 496

eth-test					0.00 496

ZD-MVS					96.63 9	65.50 185		93.50 93	70.74 292	85.26 80	95.19 83	64.92 94	97.29 89	87.51 73	93.01 60

RE-MVS-def			80.48 185		92.02 109	58.56 369	90.90 252	90.45 249	62.76 377	78.89 163	94.46 101	49.30 313		78.77 183	86.77 148	92.28 231

IU-MVS					96.46 11	69.91 43		95.18 23	80.75 66	95.28 1				92.34 35	95.36 14	96.47 28

OPU-MVS				89.97 3	97.52 3	73.15 14	96.89 6				97.00 15	83.82 2	99.15 2	95.72 8	97.63 3	97.62 2

test_241102_TWO								94.41 57	71.65 267	92.07 11	97.21 9	74.58 20	99.11 6	92.34 35	95.36 14	96.59 19

test_241102_ONE					96.45 12	69.38 59		94.44 55	71.65 267	92.11 9	97.05 12	76.79 9	99.11 6

9.14			87.63 39		93.86 52		94.41 66	94.18 66	72.76 232	86.21 65	96.51 36	66.64 72	97.88 52	90.08 55	94.04 43

save fliter					93.84 53	67.89 109	95.05 40	92.66 132	78.19 128

test_0728_THIRD									72.48 237	90.55 30	96.93 21	76.24 13	99.08 11	91.53 46	94.99 18	96.43 31

test_0728_SECOND				88.70 18	96.45 12	70.43 34	96.64 10	94.37 61					99.15 2	91.91 41	94.90 22	96.51 24

test0726					96.40 15	69.99 39	96.76 8	94.33 63	71.92 253	91.89 14	97.11 11	73.77 25

GSMVS																94.68 120

test_part2					96.29 20	68.16 102				90.78 27

sam_mvs1												57.85 207				94.68 120

sam_mvs												54.91 246

ambc				69.61 424	61.38 471	41.35 462	49.07 479	85.86 394		50.18 439	66.40 452	10.16 474	88.14 403	45.73 416	44.20 449	79.32 432

MTGPA								92.23 147

test_post1							78.95 421				20.70 485	53.05 269	91.50 368	60.43 348

test_post											23.01 482	56.49 228	92.67 328

patchmatchnet-post											67.62 451	57.62 210	90.25 378

GG-mvs-BLEND				86.53 88	91.91 118	69.67 54	75.02 440	94.75 40		78.67 171	90.85 205	77.91 7	94.56 253	72.25 236	93.74 49	95.36 72

MTMP							93.77 103	32.52 491

gm-plane-assit					88.42 218	67.04 137			78.62 122		91.83 179		97.37 83	76.57 195

test9_res														89.41 56	94.96 19	95.29 78

TEST9					94.18 45	67.28 125	94.16 75	93.51 91	71.75 264	85.52 75	95.33 71	68.01 61	97.27 93

test_8					94.19 44	67.19 130	94.15 77	93.42 98	71.87 258	85.38 78	95.35 70	68.19 59	96.95 120

agg_prior2														86.41 87	94.75 30	95.33 74

agg_prior					94.16 47	66.97 144		93.31 101		84.49 86			96.75 132

TestCases				72.46 409	79.57 391	51.42 419		68.61 462	51.25 438	45.88 450	81.23 360	19.86 461	86.58 419	38.98 441	57.01 418	79.39 430

test_prior4						67.18 132	93.92 92

test_prior2							95.10 39		75.40 181	85.25 81	95.61 62	67.94 62		87.47 75	94.77 26

test_prior				86.42 92	94.71 39	67.35 124		93.10 112					96.84 129			95.05 93

旧先验2							92.00 197		59.37 406	87.54 55			93.47 302	75.39 204

新几何2							91.41 225

新几何1				84.73 166	92.32 97	64.28 224		91.46 193	59.56 405	79.77 150	92.90 145	56.95 220	96.57 138	63.40 328	92.91 62	93.34 191

旧先验1					91.94 114	60.74 328		91.50 191			94.36 105	65.23 89			91.84 78	94.55 129

无先验							92.71 152	92.61 137	62.03 385				97.01 110	66.63 298		93.97 167

原ACMM2							92.01 194

原ACMM1				84.42 183	93.21 72	64.27 225		93.40 100	65.39 352	79.51 155	92.50 153	58.11 203	96.69 134	65.27 318	93.96 44	92.32 229

test222					89.77 167	61.60 307	89.55 299	89.42 301	56.83 421	77.28 187	92.43 157	52.76 272			91.14 95	93.09 201

testdata2													96.09 163	61.26 344

segment_acmp												65.94 80

testdata				81.34 287	89.02 191	57.72 376		89.84 283	58.65 410	85.32 79	94.09 121	57.03 215	93.28 304	69.34 265	90.56 101	93.03 204

testdata1							89.21 311		77.55 144

test12				87.09 56	94.60 40	68.86 78		92.91 120		82.67 109		65.44 86	97.55 72		93.69 52	94.84 105

plane_prior7					86.94 271	61.51 309

plane_prior6					87.23 258	62.32 287						50.66 296

plane_prior5								91.31 197					95.55 207	76.74 193	78.53 258	88.39 303

plane_prior4											89.14 246

plane_prior3						61.95 296			79.09 111	72.53 250

plane_prior2							93.13 131		78.81 118

plane_prior1					87.15 261

plane_prior						62.42 283	93.85 96		79.38 103						78.80 255

n2								0.00 497

nn								0.00 497

door-mid								66.01 466

lessismore_v0				73.72 400	72.93 443	47.83 440		61.72 472		45.86 452	73.76 424	28.63 438	89.81 388	47.75 408	31.37 471	83.53 384

LGP-MVS_train				79.56 337	84.31 334	59.37 358		89.73 289	69.49 307	64.86 347	88.42 255	38.65 382	94.30 264	72.56 233	72.76 302	85.01 370

test11								93.01 115

door								66.57 465

HQP5-MVS						63.66 250

HQP-NCC					87.54 250		94.06 80		79.80 88	74.18 224

ACMP_Plane					87.54 250		94.06 80		79.80 88	74.18 224

BP-MVS														77.63 190

HQP4-MVS										74.18 224			95.61 201			88.63 297

HQP3-MVS								91.70 183							78.90 253

HQP2-MVS												51.63 284

NP-MVS					87.41 253	63.04 267					90.30 216

MDTV_nov1_ep13_2view						59.90 350	80.13 416		67.65 334	72.79 244		54.33 255		59.83 352		92.58 220

MDTV_nov1_ep13			72.61 322		89.06 189	68.48 89	80.33 412	90.11 272	71.84 260	71.81 264	75.92 417	53.01 270	93.92 287	48.04 402	73.38 297

ACMMP++_ref															71.63 310

ACMMP++															69.72 320

Test By Simon												54.21 258

ITE_SJBPF				70.43 422	74.44 437	47.06 446		77.32 434	60.16 401	54.04 419	83.53 326	23.30 451	84.01 433	43.07 425	61.58 398	80.21 427

DeepMVS_CX				34.71 466	51.45 478	24.73 486		28.48 492	31.46 472	17.49 482	52.75 468	5.80 482	42.60 487	18.18 475	19.42 480	36.81 479

High-resolution multi-view benchmark