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.

MM		89.16 7	89.23 9	88.97 4	90.79 103	73.65 10	92.66 28	91.17 153	86.57 1	87.39 58	94.97 25	71.70 64	97.68 1	92.19 1	95.63 31	95.57 1

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 82	89.48 139	67.88 154	88.59 147	89.05 238	80.19 12	90.70 20	95.40 17	74.56 29	93.92 153	91.54 2	92.07 92	95.31 5

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 148	86.70 275	65.83 208	88.77 136	89.78 199	75.46 124	88.35 37	93.73 74	69.19 106	93.06 214	91.30 3	88.44 162	94.02 84

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 211	87.08 264	65.21 228	89.09 123	90.21 187	79.67 19	89.98 24	95.02 24	73.17 43	91.71 273	91.30 3	91.60 100	92.34 179

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 124	87.76 223	65.62 215	89.20 114	92.21 104	79.94 17	89.74 27	94.86 26	68.63 116	94.20 138	90.83 5	91.39 105	94.38 63

MGCNet		87.69 24	87.55 29	88.12 13	89.45 140	71.76 54	91.47 57	89.54 210	82.14 3	86.65 67	94.28 46	68.28 122	97.46 6	90.81 6	95.31 37	95.15 8

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 97	87.33 249	67.30 176	89.50 101	90.98 158	76.25 105	90.56 22	94.75 29	68.38 119	94.24 137	90.80 7	92.32 89	94.19 74

test_fmvsmconf_n		85.92 62	86.04 63	85.57 88	85.03 319	69.51 101	89.62 98	90.58 171	73.42 188	87.75 51	94.02 61	72.85 49	93.24 198	90.37 8	90.75 118	93.96 86

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 89	82.99 374	69.39 108	89.65 95	90.29 185	73.31 192	87.77 50	94.15 55	71.72 63	93.23 199	90.31 9	90.67 120	93.89 92

test_fmvsmconf0.01_n		84.73 90	84.52 92	85.34 94	80.25 417	69.03 111	89.47 102	89.65 206	73.24 196	86.98 63	94.27 47	66.62 140	93.23 199	90.26 10	89.95 133	93.78 101

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 194	87.12 263	66.01 201	88.56 149	89.43 214	75.59 120	89.32 28	94.32 44	72.89 47	91.21 301	90.11 11	92.33 87	93.16 137

fmvsm_s_conf0.5_n_2		84.04 100	84.11 100	83.81 185	86.17 288	65.00 236	86.96 213	87.28 295	74.35 160	88.25 40	94.23 50	61.82 209	92.60 232	89.85 12	88.09 172	93.84 95

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 155	86.26 284	67.40 172	89.18 115	89.31 223	72.50 207	88.31 38	93.86 70	69.66 95	91.96 261	89.81 13	91.05 111	93.38 122

fmvsm_s_conf0.1_n_2		83.80 108	83.79 107	83.83 183	85.62 300	64.94 241	87.03 210	86.62 319	74.32 161	87.97 48	94.33 43	60.67 233	92.60 232	89.72 14	87.79 179	93.96 86

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 55					97.53 2	89.67 15	96.44 9	94.41 60

No_MVS				89.16 1	94.34 31	75.53 2		92.99 55					97.53 2	89.67 15	96.44 9	94.41 60

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 10	80.27 10	91.35 16	94.16 54	78.35 14	96.77 28	89.59 17	94.22 65	94.67 41
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
fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 189	87.32 251	65.13 231	88.86 130	91.63 137	75.41 125	88.23 41	93.45 82	68.56 117	92.47 240	89.52 18	92.78 79	93.20 134

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 65	95.06 1	94.23 6	78.38 38	92.78 4	95.74 8	82.45 3	97.49 4	89.42 19	96.68 2	94.95 14

test_0728_THIRD									78.38 38	92.12 11	95.78 6	81.46 8	97.40 9	89.42 19	96.57 7	94.67 41

APDe-MVS		89.15 8	89.63 7	87.73 31	94.49 22	71.69 55	93.83 4	93.96 17	75.70 118	91.06 19	96.03 1	76.84 18	97.03 20	89.09 21	95.65 30	94.47 59
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 176	87.78 220	66.09 198	89.96 86	90.80 166	77.37 58	86.72 66	94.20 52	72.51 52	92.78 228	89.08 22	92.33 87	93.13 141

DVP-MVS		89.60 4	90.35 4	87.33 45	95.27 5	71.25 65	93.49 10	92.73 70	77.33 59	92.12 11	95.78 6	80.98 10	97.40 9	89.08 22	96.41 12	93.33 126
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
test_0728_SECOND				87.71 35	95.34 1	71.43 61	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 73

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 73	93.57 8	94.06 14	77.24 64	93.10 1	95.72 10	82.99 1	97.44 7	89.07 25	96.63 4	94.88 18

test_241102_TWO								94.06 14	77.24 64	92.78 4	95.72 10	81.26 9	97.44 7	89.07 25	96.58 6	94.26 72

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 100	87.20 255	68.54 131	89.57 99	90.44 176	75.31 129	87.49 55	94.39 42	72.86 48	92.72 229	89.04 27	90.56 121	94.16 75

IU-MVS					95.30 2	71.25 65		92.95 61	66.81 331	92.39 6				88.94 28	96.63 4	94.85 23

fmvsm_l_conf0.5_n		84.47 91	84.54 90	84.27 152	85.42 306	68.81 117	88.49 152	87.26 300	68.08 319	88.03 45	93.49 78	72.04 59	91.77 269	88.90 29	89.14 149	92.24 186

fmvsm_s_conf0.5_n		83.80 108	83.71 109	84.07 165	86.69 276	67.31 175	89.46 103	83.07 373	71.09 238	86.96 64	93.70 75	69.02 112	91.47 289	88.79 30	84.62 239	93.44 121

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 51	89.80 90	93.50 30	75.17 137	86.34 69	95.29 19	70.86 76	96.00 60	88.78 31	96.04 16	94.58 50
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvsmvis_n_1920		84.02 101	83.87 103	84.49 135	84.12 337	69.37 109	88.15 169	87.96 277	70.01 272	83.95 109	93.23 87	68.80 114	91.51 286	88.61 32	89.96 132	92.57 166

SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 150	92.29 7	95.97 2	74.28 34	97.24 15	88.58 33	96.91 1	94.87 20
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
fmvsm_s_conf0.1_n		83.56 120	83.38 118	84.10 159	84.86 321	67.28 177	89.40 108	83.01 374	70.67 251	87.08 61	93.96 67	68.38 119	91.45 290	88.56 34	84.50 240	93.56 116

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 103	86.12 290	69.93 93	88.65 145	90.78 167	69.97 274	88.27 39	93.98 66	71.39 69	91.54 283	88.49 35	90.45 123	93.91 89

CNVR-MVS		88.93 12	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 52	80.90 7	88.06 44	94.06 59	76.43 20	96.84 25	88.48 36	95.99 18	94.34 66

fmvsm_l_conf0.5_n_a		84.13 98	84.16 95	84.06 168	85.38 307	68.40 134	88.34 160	86.85 312	67.48 326	87.48 56	93.40 83	70.89 75	91.61 274	88.38 37	89.22 146	92.16 193

fmvsm_s_conf0.5_n_a		83.63 117	83.41 117	84.28 150	86.14 289	68.12 144	89.43 104	82.87 378	70.27 267	87.27 60	93.80 73	69.09 107	91.58 276	88.21 38	83.65 260	93.14 140

MED-MVS test				87.86 27	94.57 17	71.43 61	93.28 12	94.36 3	75.24 130	92.25 9	95.03 22		97.39 11	88.15 39	95.96 19	94.75 34

MED-MVS		89.75 3	90.37 3	87.89 24	94.57 17	71.43 61	93.28 12	94.36 3	77.30 61	92.25 9	95.87 3	81.59 7	97.39 11	88.15 39	95.96 19	94.85 23

ME-MVS		88.98 11	89.39 8	87.75 30	94.54 20	71.43 61	91.61 49	94.25 5	76.30 103	90.62 21	95.03 22	78.06 15	97.07 19	88.15 39	95.96 19	94.75 34

fmvsm_s_conf0.1_n_a		83.32 129	82.99 126	84.28 150	83.79 345	68.07 146	89.34 111	82.85 379	69.80 278	87.36 59	94.06 59	68.34 121	91.56 279	87.95 42	83.46 266	93.21 132

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 89	74.62 154	88.90 33	93.85 71	75.75 24	96.00 60	87.80 43	94.63 53	95.04 11
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 104	88.14 42	95.09 21	71.06 74	96.67 33	87.67 44	96.37 14	94.09 80

SD-MVS		88.06 18	88.50 18	86.71 61	92.60 76	72.71 29	91.81 46	93.19 41	77.87 43	90.32 23	94.00 63	74.83 27	93.78 161	87.63 45	94.27 64	93.65 109
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
SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 79	72.96 25	93.73 5	93.67 25	80.19 12	88.10 43	94.80 27	73.76 38	97.11 17	87.51 46	95.82 24	94.90 17
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		89.02 10	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 65	80.26 11	87.78 49	94.27 47	75.89 23	96.81 27	87.45 47	96.44 9	93.05 147

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 12	75.90 111	92.29 7	95.66 12	81.67 6	97.38 13	87.44 48	96.34 15	93.95 88
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SF-MVS		88.46 15	88.74 15	87.64 39	92.78 71	71.95 52	92.40 29	94.74 2	75.71 116	89.16 29	95.10 20	75.65 25	96.19 52	87.07 49	96.01 17	94.79 27

lecture		88.09 17	88.59 16	86.58 63	93.26 56	69.77 97	93.70 6	94.16 8	77.13 69	89.76 26	95.52 16	72.26 54	96.27 49	86.87 50	94.65 51	93.70 104

9.14			88.26 19		92.84 70		91.52 56	94.75 1	73.93 173	88.57 36	94.67 30	75.57 26	95.79 64	86.77 51	95.76 26

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 231	92.02 113	79.45 22	85.88 71	94.80 27	68.07 124	96.21 51	86.69 52	95.34 35	93.23 129

fmvsm_s_conf0.5_n_7		83.34 127	84.03 101	81.28 272	85.73 297	65.13 231	85.40 272	89.90 197	74.96 143	82.13 147	93.89 69	66.65 139	87.92 375	86.56 53	91.05 111	90.80 235

reproduce-ours		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 141	88.96 30	95.54 14	71.20 72	96.54 41	86.28 54	93.49 70	93.06 145

our_new_method		87.47 27	87.61 27	87.07 51	93.27 54	71.60 56	91.56 54	93.19 41	74.98 141	88.96 30	95.54 14	71.20 72	96.54 41	86.28 54	93.49 70	93.06 145

reproduce_model		87.28 35	87.39 33	86.95 55	93.10 62	71.24 70	91.60 50	93.19 41	74.69 151	88.80 34	95.61 13	70.29 83	96.44 44	86.20 56	93.08 74	93.16 137

TestfortrainingZip a		88.83 13	89.21 11	87.68 37	94.57 17	71.25 65	93.28 12	93.91 19	77.30 61	91.13 18	95.87 3	77.62 16	96.95 22	86.12 57	93.07 75	94.85 23

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 60	92.24 78	69.03 111	89.57 99	93.39 35	77.53 54	89.79 25	94.12 56	78.98 13	96.58 40	85.66 58	95.72 27	94.58 50

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 76	77.57 50	83.84 111	94.40 41	72.24 55	96.28 48	85.65 59	95.30 38	93.62 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
BridgeMVS		86.78 42	86.99 40	86.15 71	91.24 91	67.61 163	90.51 70	92.90 62	77.26 63	87.44 57	91.63 138	71.27 71	96.06 55	85.62 60	95.01 40	94.78 28

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 79	85.24 78	94.32 44	71.76 62	96.93 23	85.53 61	95.79 25	94.32 68

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 108	83.81 112	93.95 68	69.77 94	96.01 59	85.15 62	94.66 50	94.32 68
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
train_agg		86.43 49	86.20 56	87.13 50	93.26 56	72.96 25	88.75 138	91.89 121	68.69 310	85.00 81	93.10 89	74.43 31	95.41 81	84.97 63	95.71 28	93.02 149

test9_res														84.90 64	95.70 29	92.87 156

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 66	81.50 5	85.79 73	93.47 81	73.02 46	97.00 21	84.90 64	94.94 43	94.10 79

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 200	84.86 86	92.89 96	76.22 21	96.33 46	84.89 66	95.13 39	94.40 62

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 92	72.32 45	90.31 79	93.94 18	77.12 70	82.82 137	94.23 50	72.13 58	97.09 18	84.83 67	95.37 34	93.65 109
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 43	86.67 48	86.91 56	94.11 41	72.11 49	92.37 33	92.56 81	74.50 155	86.84 65	94.65 31	67.31 132	95.77 65	84.80 68	92.85 78	92.84 159

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 74	91.09 93	67.64 162	89.63 97	92.65 76	72.89 205	84.64 91	91.71 133	71.85 60	96.03 56	84.77 69	94.45 59	94.49 58

ZD-MVS					94.38 29	72.22 46		92.67 73	70.98 243	87.75 51	94.07 58	74.01 37	96.70 31	84.66 70	94.84 47

PC_three_1452									68.21 318	92.02 14	94.00 63	82.09 5	95.98 62	84.58 71	96.68 2	94.95 14

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 39	76.78 81	84.91 83	94.44 39	70.78 77	96.61 37	84.53 72	94.89 45	93.66 105

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 40	76.78 81	84.66 90	94.52 32	68.81 113	96.65 35	84.53 72	94.90 44	94.00 85

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 46	76.73 84	84.45 95	94.52 32	69.09 107	96.70 31	84.37 74	94.83 48	94.03 83

CANet		86.45 48	86.10 61	87.51 42	90.09 116	70.94 77	89.70 94	92.59 80	81.78 4	81.32 161	91.43 148	70.34 81	97.23 16	84.26 75	93.36 73	94.37 64

APD-MVS		87.44 29	87.52 30	87.19 48	94.24 36	72.39 41	91.86 45	92.83 66	73.01 202	88.58 35	94.52 32	73.36 39	96.49 43	84.26 75	95.01 40	92.70 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CS-MVS		86.69 44	86.95 42	85.90 79	90.76 104	67.57 165	92.83 22	93.30 38	79.67 19	84.57 94	92.27 109	71.47 67	95.02 101	84.24 77	93.46 72	95.13 10

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 69	76.62 87	83.68 114	94.46 36	67.93 125	95.95 63	84.20 78	94.39 60	93.23 129

GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 77	84.68 87	93.99 65	70.67 79	96.82 26	84.18 79	95.01 40	93.90 91

BP-MVS1		84.32 92	83.71 109	86.17 69	87.84 215	67.85 155	89.38 109	89.64 207	77.73 46	83.98 108	92.12 120	56.89 271	95.43 78	84.03 80	91.75 99	95.24 7

EC-MVSNet		86.01 58	86.38 52	84.91 115	89.31 149	66.27 196	92.32 35	93.63 26	79.37 23	84.17 104	91.88 125	69.04 111	95.43 78	83.93 81	93.77 68	93.01 150

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 18

casdiffmvs_mvg		85.99 59	86.09 62	85.70 82	87.65 231	67.22 181	88.69 143	93.04 47	79.64 21	85.33 77	92.54 105	73.30 40	94.50 126	83.49 83	91.14 110	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
dcpmvs_2		85.63 70	86.15 60	84.06 168	91.71 85	64.94 241	86.47 235	91.87 123	73.63 180	86.60 68	93.02 94	76.57 19	91.87 267	83.36 84	92.15 90	95.35 3

test_prior2							88.85 132		75.41 125	84.91 83	93.54 76	74.28 34		83.31 85	95.86 23

PHI-MVS		86.43 49	86.17 59	87.24 47	90.88 100	70.96 75	92.27 37	94.07 13	72.45 208	85.22 79	91.90 124	69.47 97	96.42 45	83.28 86	95.94 22	94.35 65

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 26	83.67 115	94.17 53	67.45 130	96.60 38	83.06 87	94.50 56	94.07 81

X-MVStestdata		80.37 203	77.83 243	88.00 17	94.42 24	73.33 19	92.78 23	92.99 55	79.14 26	83.67 115	12.47 515	67.45 130	96.60 38	83.06 87	94.50 56	94.07 81

balanced_ft_v1		83.98 104	83.64 112	85.03 106	89.76 129	65.86 207	88.31 162	91.71 133	74.41 159	80.41 183	90.82 170	62.90 191	94.90 105	83.04 89	91.37 106	94.32 68

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 68	92.69 73	69.53 100	91.93 42	92.99 55	73.54 184	85.94 70	94.51 35	65.80 156	95.61 68	83.04 89	92.51 83	93.53 119

agg_prior2														82.91 91	95.45 32	92.70 161

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 102	76.87 78	82.81 138	94.25 49	66.44 144	96.24 50	82.88 92	94.28 63	93.38 122

diffmvs_AUTHOR		82.38 146	82.27 142	82.73 237	83.26 359	63.80 270	83.89 314	89.76 201	73.35 191	82.37 142	90.84 168	66.25 147	90.79 320	82.77 93	87.93 177	93.59 114

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 67	93.06 64	70.63 83	91.88 43	92.27 95	73.53 185	85.69 74	94.45 37	65.00 164	95.56 69	82.75 94	91.87 96	92.50 172

RE-MVS-def			85.48 75		93.06 64	70.63 83	91.88 43	92.27 95	73.53 185	85.69 74	94.45 37	63.87 174		82.75 94	91.87 96	92.50 172

h-mvs33		83.15 132	82.19 143	86.02 77	90.56 106	70.85 80	88.15 169	89.16 233	76.02 109	84.67 88	91.39 149	61.54 214	95.50 74	82.71 96	75.48 373	91.72 206

hse-mvs2		81.72 158	80.94 163	84.07 165	88.72 177	67.68 161	85.87 257	87.26 300	76.02 109	84.67 88	88.22 252	61.54 214	93.48 186	82.71 96	73.44 401	91.06 225

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 47	75.53 121	83.86 110	94.42 40	67.87 127	96.64 36	82.70 98	94.57 55	93.66 105

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 45	76.78 81	80.73 176	93.82 72	64.33 170	96.29 47	82.67 99	90.69 119	93.23 129
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
diffmvs		82.10 149	81.88 151	82.76 235	83.00 370	63.78 272	83.68 319	89.76 201	72.94 203	82.02 149	89.85 196	65.96 155	90.79 320	82.38 100	87.30 189	93.71 103
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
patch_mono-2		83.65 115	84.54 90	80.99 281	90.06 121	65.83 208	84.21 307	88.74 257	71.60 226	85.01 80	92.44 107	74.51 30	83.50 422	82.15 101	92.15 90	93.64 111

SPE-MVS-test		86.29 54	86.48 51	85.71 81	91.02 96	67.21 182	92.36 34	93.78 23	78.97 33	83.51 122	91.20 156	70.65 80	95.15 92	81.96 102	94.89 45	94.77 29

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 57	91.34 89	72.50 36	89.07 124	87.28 295	76.41 95	85.80 72	90.22 191	74.15 36	95.37 86	81.82 103	91.88 95	92.65 165

alignmvs		85.48 73	85.32 79	85.96 78	89.51 136	69.47 103	89.74 92	92.47 82	76.17 106	87.73 53	91.46 147	70.32 82	93.78 161	81.51 104	88.95 150	94.63 47

sasdasda		85.91 63	85.87 67	86.04 75	89.84 126	69.44 106	90.45 76	93.00 52	76.70 85	88.01 46	91.23 152	73.28 41	93.91 154	81.50 105	88.80 153	94.77 29

canonicalmvs		85.91 63	85.87 67	86.04 75	89.84 126	69.44 106	90.45 76	93.00 52	76.70 85	88.01 46	91.23 152	73.28 41	93.91 154	81.50 105	88.80 153	94.77 29

baseline		84.93 87	84.98 84	84.80 121	87.30 253	65.39 221	87.30 203	92.88 63	77.62 48	84.04 107	92.26 110	71.81 61	93.96 146	81.31 107	90.30 125	95.03 12

MGCFI-Net		85.06 86	85.51 74	83.70 187	89.42 141	63.01 294	89.43 104	92.62 79	76.43 94	87.53 54	91.34 150	72.82 50	93.42 191	81.28 108	88.74 156	94.66 44

casdiffmvs		85.11 83	85.14 83	85.01 108	87.20 255	65.77 212	87.75 183	92.83 66	77.84 44	84.36 100	92.38 108	72.15 57	93.93 152	81.27 109	90.48 122	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_111021_HR		85.14 82	84.75 88	86.32 66	91.65 86	72.70 30	85.98 253	90.33 182	76.11 107	82.08 148	91.61 141	71.36 70	94.17 141	81.02 110	92.58 82	92.08 195

HPM-MVS_fast		85.35 79	84.95 86	86.57 64	93.69 46	70.58 85	92.15 40	91.62 138	73.89 174	82.67 141	94.09 57	62.60 193	95.54 71	80.93 111	92.93 77	93.57 115

CPTT-MVS		83.73 112	83.33 120	84.92 114	93.28 53	70.86 79	92.09 41	90.38 178	68.75 309	79.57 193	92.83 98	60.60 237	93.04 217	80.92 112	91.56 103	90.86 234

ETV-MVS		84.90 89	84.67 89	85.59 87	89.39 144	68.66 128	88.74 140	92.64 78	79.97 16	84.10 105	85.71 321	69.32 100	95.38 83	80.82 113	91.37 106	92.72 160

DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 47	76.62 87	84.22 102	93.36 85	71.44 68	96.76 29	80.82 113	95.33 36	94.16 75
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
nrg030		83.88 106	83.53 115	84.96 110	86.77 273	69.28 110	90.46 75	92.67 73	74.79 149	82.95 132	91.33 151	72.70 51	93.09 212	80.79 115	79.28 322	92.50 172

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 107	79.31 24	84.39 97	92.18 115	64.64 167	95.53 72	80.70 116	94.65 51	94.56 54

SymmetryMVS		85.38 78	84.81 87	87.07 51	91.47 88	72.47 38	91.65 47	88.06 273	79.31 24	84.39 97	92.18 115	64.64 167	95.53 72	80.70 116	90.91 116	93.21 132

EI-MVSNet-Vis-set		84.19 97	83.81 106	85.31 95	88.18 196	67.85 155	87.66 185	89.73 204	80.05 15	82.95 132	89.59 209	70.74 78	94.82 110	80.66 118	84.72 237	93.28 128

hybridcas		85.11 83	85.18 82	84.90 116	87.47 243	65.68 213	88.53 151	92.38 87	77.91 42	84.27 101	92.48 106	72.19 56	93.88 158	80.37 119	90.97 113	95.15 8

MSLP-MVS++		85.43 75	85.76 69	84.45 136	91.93 82	70.24 86	90.71 67	92.86 64	77.46 56	84.22 102	92.81 100	67.16 134	92.94 219	80.36 120	94.35 62	90.16 264

MVS_111021_LR		82.61 143	82.11 144	84.11 158	88.82 168	71.58 58	85.15 277	86.16 327	74.69 151	80.47 182	91.04 162	62.29 200	90.55 327	80.33 121	90.08 130	90.20 263

DELS-MVS		85.41 76	85.30 80	85.77 80	88.49 184	67.93 153	85.52 271	93.44 32	78.70 34	83.63 117	89.03 224	74.57 28	95.71 67	80.26 122	94.04 66	93.66 105
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
GDP-MVS		83.52 121	82.64 133	86.16 70	88.14 199	68.45 133	89.13 121	92.69 71	72.82 206	83.71 113	91.86 127	55.69 280	95.35 87	80.03 123	89.74 137	94.69 36

EI-MVSNet-UG-set		83.81 107	83.38 118	85.09 105	87.87 213	67.53 167	87.44 198	89.66 205	79.74 18	82.23 145	89.41 218	70.24 84	94.74 116	79.95 124	83.92 252	92.99 152

CSCG		86.41 51	86.19 58	87.07 51	92.91 67	72.48 37	90.81 66	93.56 29	73.95 171	83.16 129	91.07 161	75.94 22	95.19 90	79.94 125	94.38 61	93.55 117

E5new		84.22 93	84.12 96	84.51 131	87.60 233	65.36 223	87.45 193	92.31 91	76.51 90	83.53 118	92.26 110	69.25 104	93.50 181	79.88 126	88.26 164	94.69 36

E6new		84.22 93	84.12 96	84.52 129	87.60 233	65.36 223	87.45 193	92.30 93	76.51 90	83.53 118	92.26 110	69.26 102	93.49 183	79.88 126	88.26 164	94.69 36

E6		84.22 93	84.12 96	84.52 129	87.60 233	65.36 223	87.45 193	92.30 93	76.51 90	83.53 118	92.26 110	69.26 102	93.49 183	79.88 126	88.26 164	94.69 36

E5		84.22 93	84.12 96	84.51 131	87.60 233	65.36 223	87.45 193	92.31 91	76.51 90	83.53 118	92.26 110	69.25 104	93.50 181	79.88 126	88.26 164	94.69 36

RRT-MVS		82.60 145	82.10 145	84.10 159	87.98 209	62.94 300	87.45 193	91.27 149	77.42 57	79.85 189	90.28 187	56.62 274	94.70 119	79.87 130	88.15 170	94.67 41

E4		84.10 99	83.99 102	84.45 136	87.58 241	64.99 237	86.54 233	92.25 98	76.38 99	83.37 123	92.09 121	69.88 92	93.58 170	79.78 131	88.03 175	94.77 29

AstraMVS		80.81 182	80.14 183	82.80 229	86.05 292	63.96 265	86.46 236	85.90 331	73.71 178	80.85 174	90.56 178	54.06 297	91.57 278	79.72 132	83.97 251	92.86 157

OPM-MVS		83.50 122	82.95 127	85.14 100	88.79 174	70.95 76	89.13 121	91.52 142	77.55 53	80.96 170	91.75 131	60.71 231	94.50 126	79.67 133	86.51 204	89.97 280
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
hybrid		81.05 176	80.66 168	82.22 248	81.97 392	62.99 298	83.42 328	88.68 259	70.76 249	80.56 179	90.40 183	64.49 169	90.48 328	79.57 134	86.06 213	93.19 135

E2		84.00 102	83.87 103	84.39 139	87.70 228	64.95 238	86.40 240	92.23 99	75.85 112	83.21 125	91.78 129	70.09 87	93.55 175	79.52 135	88.05 173	94.66 44

E3		84.00 102	83.87 103	84.39 139	87.70 228	64.95 238	86.40 240	92.23 99	75.85 112	83.21 125	91.78 129	70.09 87	93.55 175	79.52 135	88.05 173	94.66 44

viewcassd2359sk11		83.89 105	83.74 108	84.34 144	87.76 223	64.91 244	86.30 244	92.22 102	75.47 123	83.04 131	91.52 143	70.15 85	93.53 178	79.26 137	87.96 176	94.57 52

E3new		83.78 110	83.60 113	84.31 146	87.76 223	64.89 245	86.24 247	92.20 105	75.15 138	82.87 134	91.23 152	70.11 86	93.52 180	79.05 138	87.79 179	94.51 57

viewmacassd2359aftdt		83.76 111	83.66 111	84.07 165	86.59 279	64.56 250	86.88 218	91.82 126	75.72 115	83.34 124	92.15 119	68.24 123	92.88 222	79.05 138	89.15 148	94.77 29

viewmanbaseed2359cas		83.66 114	83.55 114	84.00 176	86.81 271	64.53 251	86.65 228	91.75 131	74.89 145	83.15 130	91.68 134	68.74 115	92.83 226	79.02 140	89.24 145	94.63 47

LuminaMVS		80.68 190	79.62 199	83.83 183	85.07 318	68.01 149	86.99 212	88.83 248	70.36 262	81.38 160	87.99 260	50.11 351	92.51 239	79.02 140	86.89 198	90.97 230

CDPH-MVS		85.76 68	85.29 81	87.17 49	93.49 51	71.08 71	88.58 148	92.42 86	68.32 317	84.61 92	93.48 79	72.32 53	96.15 54	79.00 142	95.43 33	94.28 71

MVSFormer		82.85 139	82.05 147	85.24 97	87.35 244	70.21 87	90.50 72	90.38 178	68.55 312	81.32 161	89.47 212	61.68 211	93.46 188	78.98 143	90.26 126	92.05 196

test_djsdf		80.30 206	79.32 208	83.27 202	83.98 341	65.37 222	90.50 72	90.38 178	68.55 312	76.19 273	88.70 235	56.44 275	93.46 188	78.98 143	80.14 310	90.97 230

test_vis1_n_1920		75.52 314	75.78 288	74.75 401	79.84 424	57.44 388	83.26 333	85.52 335	62.83 395	79.34 200	86.17 314	45.10 404	79.71 445	78.75 145	81.21 294	87.10 378

HQP_MVS		83.64 116	83.14 121	85.14 100	90.08 117	68.71 124	91.25 60	92.44 83	79.12 28	78.92 205	91.00 165	60.42 239	95.38 83	78.71 146	86.32 206	91.33 217

plane_prior5								92.44 83					95.38 83	78.71 146	86.32 206	91.33 217

LPG-MVS_test		82.08 150	81.27 156	84.50 133	89.23 154	68.76 120	90.22 81	91.94 119	75.37 127	76.64 261	91.51 144	54.29 293	94.91 103	78.44 148	83.78 253	89.83 285

LGP-MVS_train				84.50 133	89.23 154	68.76 120		91.94 119	75.37 127	76.64 261	91.51 144	54.29 293	94.91 103	78.44 148	83.78 253	89.83 285

lupinMVS		81.39 170	80.27 179	84.76 123	87.35 244	70.21 87	85.55 267	86.41 321	62.85 394	81.32 161	88.61 239	61.68 211	92.24 252	78.41 150	90.26 126	91.83 199

jason		81.39 170	80.29 178	84.70 125	86.63 278	69.90 95	85.95 254	86.77 313	63.24 387	81.07 167	89.47 212	61.08 227	92.15 254	78.33 151	90.07 131	92.05 196
jason: jason.
xiu_mvs_v1_base_debu		80.80 185	79.72 196	84.03 173	87.35 244	70.19 89	85.56 264	88.77 251	69.06 300	81.83 150	88.16 253	50.91 339	92.85 223	78.29 152	87.56 183	89.06 305

xiu_mvs_v1_base		80.80 185	79.72 196	84.03 173	87.35 244	70.19 89	85.56 264	88.77 251	69.06 300	81.83 150	88.16 253	50.91 339	92.85 223	78.29 152	87.56 183	89.06 305

xiu_mvs_v1_base_debi		80.80 185	79.72 196	84.03 173	87.35 244	70.19 89	85.56 264	88.77 251	69.06 300	81.83 150	88.16 253	50.91 339	92.85 223	78.29 152	87.56 183	89.06 305

guyue		81.13 174	80.64 169	82.60 240	86.52 280	63.92 268	86.69 227	87.73 285	73.97 170	80.83 175	89.69 203	56.70 272	91.33 295	78.26 155	85.40 229	92.54 168

Effi-MVS+		83.62 118	83.08 122	85.24 97	88.38 190	67.45 169	88.89 129	89.15 234	75.50 122	82.27 144	88.28 249	69.61 96	94.45 129	77.81 156	87.84 178	93.84 95

KinetiMVS		83.31 130	82.61 134	85.39 93	87.08 264	67.56 166	88.06 171	91.65 136	77.80 45	82.21 146	91.79 128	57.27 266	94.07 144	77.77 157	89.89 135	94.56 54

viewdifsd2359ckpt07		82.83 140	82.78 132	82.99 218	86.51 281	62.58 304	85.09 280	90.83 165	75.22 131	82.28 143	91.63 138	69.43 98	92.03 257	77.71 158	86.32 206	94.34 66

PS-MVSNAJss		82.07 151	81.31 155	84.34 144	86.51 281	67.27 178	89.27 112	91.51 143	71.75 221	79.37 198	90.22 191	63.15 184	94.27 133	77.69 159	82.36 281	91.49 213

ACMP	74.13 6	81.51 169	80.57 170	84.36 142	89.42 141	68.69 127	89.97 85	91.50 146	74.46 157	75.04 308	90.41 182	53.82 299	94.54 123	77.56 160	82.91 273	89.86 284
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS														77.47 161

HQP-MVS		82.61 143	82.02 148	84.37 141	89.33 146	66.98 185	89.17 116	92.19 107	76.41 95	77.23 246	90.23 190	60.17 242	95.11 95	77.47 161	85.99 216	91.03 227

MVS_Test		83.15 132	83.06 123	83.41 198	86.86 268	63.21 290	86.11 251	92.00 115	74.31 162	82.87 134	89.44 217	70.03 89	93.21 201	77.39 163	88.50 161	93.81 97

3Dnovator+	77.84 4	85.48 73	84.47 93	88.51 7	91.08 94	73.49 16	93.18 16	93.78 23	80.79 8	76.66 260	93.37 84	60.40 241	96.75 30	77.20 164	93.73 69	95.29 6

anonymousdsp		78.60 248	77.15 263	82.98 220	80.51 415	67.08 183	87.24 205	89.53 211	65.66 352	75.16 303	87.19 282	52.52 308	92.25 251	77.17 165	79.34 321	89.61 292

mmtdpeth		74.16 330	73.01 334	77.60 369	83.72 348	61.13 331	85.10 279	85.10 340	72.06 217	77.21 250	80.33 420	43.84 413	85.75 398	77.14 166	52.61 481	85.91 403

VDD-MVS		83.01 137	82.36 139	84.96 110	91.02 96	66.40 193	88.91 128	88.11 269	77.57 50	84.39 97	93.29 86	52.19 314	93.91 154	77.05 167	88.70 157	94.57 52

XVG-OURS-SEG-HR		80.81 182	79.76 193	83.96 180	85.60 301	68.78 119	83.54 327	90.50 174	70.66 254	76.71 259	91.66 135	60.69 232	91.26 296	76.94 168	81.58 290	91.83 199

Elysia		81.53 165	80.16 181	85.62 85	85.51 303	68.25 140	88.84 133	92.19 107	71.31 231	80.50 180	89.83 197	46.89 382	94.82 110	76.85 169	89.57 139	93.80 99

StellarMVS		81.53 165	80.16 181	85.62 85	85.51 303	68.25 140	88.84 133	92.19 107	71.31 231	80.50 180	89.83 197	46.89 382	94.82 110	76.85 169	89.57 139	93.80 99

jajsoiax		79.29 230	77.96 237	83.27 202	84.68 326	66.57 192	89.25 113	90.16 189	69.20 296	75.46 288	89.49 211	45.75 399	93.13 210	76.84 171	80.80 300	90.11 268

SDMVSNet		80.38 201	80.18 180	80.99 281	89.03 163	64.94 241	80.45 382	89.40 215	75.19 135	76.61 263	89.98 193	60.61 236	87.69 379	76.83 172	83.55 262	90.33 258

viewdifsd2359ckpt11		80.37 203	79.73 194	82.30 246	83.70 349	62.39 308	84.20 308	86.67 315	73.22 197	80.90 171	90.62 175	63.00 189	91.56 279	76.81 173	78.44 329	92.95 154

viewmsd2359difaftdt		80.37 203	79.73 194	82.30 246	83.70 349	62.39 308	84.20 308	86.67 315	73.22 197	80.90 171	90.62 175	63.00 189	91.56 279	76.81 173	78.44 329	92.95 154

mvs_tets		79.13 234	77.77 247	83.22 206	84.70 325	66.37 194	89.17 116	90.19 188	69.38 288	75.40 291	89.46 214	44.17 411	93.15 208	76.78 175	80.70 302	90.14 265

DPM-MVS		84.93 87	84.29 94	86.84 57	90.20 114	73.04 23	87.12 207	93.04 47	69.80 278	82.85 136	91.22 155	73.06 45	96.02 58	76.72 176	94.63 53	91.46 216

test_cas_vis1_n_1920		73.76 336	73.74 325	73.81 412	75.90 457	59.77 357	80.51 380	82.40 383	58.30 439	81.62 158	85.69 322	44.35 410	76.41 463	76.29 177	78.61 325	85.23 415

ET-MVSNet_ETH3D		78.63 247	76.63 278	84.64 126	86.73 274	69.47 103	85.01 282	84.61 346	69.54 285	66.51 425	86.59 300	50.16 350	91.75 270	76.26 178	84.24 248	92.69 163

viewdifsd2359ckpt09		83.34 127	82.55 135	85.70 82	87.64 232	67.72 160	88.43 153	91.68 135	71.91 220	81.65 157	90.68 173	67.10 135	94.75 115	76.17 179	87.70 182	94.62 49

v2v482		80.23 207	79.29 209	83.05 215	83.62 351	64.14 262	87.04 209	89.97 194	73.61 181	78.18 224	87.22 280	61.10 226	93.82 159	76.11 180	76.78 352	91.18 221

test_fmvs1_n		70.86 378	70.24 373	72.73 423	72.51 481	55.28 420	81.27 368	79.71 422	51.49 471	78.73 207	84.87 344	27.54 477	77.02 457	76.06 181	79.97 312	85.88 404

CLD-MVS		82.31 147	81.65 153	84.29 149	88.47 185	67.73 159	85.81 261	92.35 89	75.78 114	78.33 220	86.58 302	64.01 173	94.35 130	76.05 182	87.48 186	90.79 236
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		83.72 113	82.92 128	86.14 73	84.22 335	69.48 102	91.05 64	85.27 337	81.30 6	76.83 255	91.65 136	66.09 151	95.56 69	76.00 183	93.85 67	93.38 122
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		82.91 138	82.29 141	84.77 122	86.96 267	66.90 189	87.47 190	91.62 138	72.19 213	81.68 156	90.71 172	66.92 136	93.28 194	75.90 184	87.15 192	94.12 78

test_fmvs1		70.93 376	70.52 368	72.16 426	73.71 469	55.05 422	80.82 371	78.77 431	51.21 472	78.58 212	84.41 352	31.20 471	76.94 458	75.88 185	80.12 311	84.47 427

XVG-OURS		80.41 199	79.23 211	83.97 179	85.64 299	69.02 113	83.03 342	90.39 177	71.09 238	77.63 237	91.49 146	54.62 292	91.35 293	75.71 186	83.47 265	91.54 210

V42		79.38 228	78.24 233	82.83 226	81.10 409	65.50 218	85.55 267	89.82 198	71.57 227	78.21 222	86.12 315	60.66 234	93.18 207	75.64 187	75.46 375	89.81 287

PS-MVSNAJ		81.69 160	81.02 161	83.70 187	89.51 136	68.21 143	84.28 306	90.09 191	70.79 247	81.26 165	85.62 326	63.15 184	94.29 131	75.62 188	88.87 152	88.59 329

xiu_mvs_v2_base		81.69 160	81.05 160	83.60 189	89.15 157	68.03 148	84.46 298	90.02 192	70.67 251	81.30 164	86.53 305	63.17 183	94.19 140	75.60 189	88.54 159	88.57 330

EIA-MVS		83.31 130	82.80 130	84.82 119	89.59 132	65.59 216	88.21 165	92.68 72	74.66 153	78.96 203	86.42 307	69.06 109	95.26 88	75.54 190	90.09 129	93.62 112

AUN-MVS		79.21 232	77.60 253	84.05 171	88.71 178	67.61 163	85.84 259	87.26 300	69.08 299	77.23 246	88.14 257	53.20 306	93.47 187	75.50 191	73.45 400	91.06 225

mvsmamba		80.60 194	79.38 205	84.27 152	89.74 130	67.24 180	87.47 190	86.95 308	70.02 271	75.38 292	88.93 229	51.24 336	92.56 235	75.47 192	89.22 146	93.00 151

reproduce_monomvs		75.40 318	74.38 316	78.46 350	83.92 343	57.80 381	83.78 316	86.94 309	73.47 187	72.25 349	84.47 350	38.74 446	89.27 351	75.32 193	70.53 420	88.31 335

OMC-MVS		82.69 141	81.97 150	84.85 118	88.75 176	67.42 170	87.98 173	90.87 163	74.92 144	79.72 191	91.65 136	62.19 203	93.96 146	75.26 194	86.42 205	93.16 137

VortexMVS		78.57 250	77.89 241	80.59 290	85.89 293	62.76 302	85.61 262	89.62 208	72.06 217	74.99 309	85.38 332	55.94 279	90.77 323	74.99 195	76.58 353	88.23 338

v1144		80.03 212	79.03 215	83.01 217	83.78 346	64.51 253	87.11 208	90.57 173	71.96 219	78.08 227	86.20 313	61.41 218	93.94 149	74.93 196	77.23 343	90.60 246

MVSTER		79.01 237	77.88 242	82.38 244	83.07 367	64.80 247	84.08 313	88.95 245	69.01 303	78.69 208	87.17 283	54.70 290	92.43 242	74.69 197	80.57 304	89.89 283

viewmambaseed2359dif		80.41 199	79.84 191	82.12 249	82.95 376	62.50 307	83.39 329	88.06 273	67.11 329	80.98 169	90.31 186	66.20 149	91.01 310	74.62 198	84.90 233	92.86 157

test_vis1_n		69.85 395	69.21 381	71.77 429	72.66 480	55.27 421	81.48 362	76.21 451	52.03 468	75.30 299	83.20 384	28.97 474	76.22 465	74.60 199	78.41 333	83.81 435

test_fmvs2		68.35 409	67.48 405	70.98 438	69.50 485	51.95 447	80.05 389	76.38 450	49.33 474	74.65 316	84.38 353	23.30 486	75.40 474	74.51 200	75.17 384	85.60 408

PVSNet_Blended_VisFu		82.62 142	81.83 152	84.96 110	90.80 102	69.76 98	88.74 140	91.70 134	69.39 287	78.96 203	88.46 244	65.47 158	94.87 109	74.42 201	88.57 158	90.24 262

v8		79.97 214	79.02 216	82.80 229	84.09 338	64.50 255	87.96 174	90.29 185	74.13 169	75.24 301	86.81 289	62.88 192	93.89 157	74.39 202	75.40 378	90.00 276

v144192		79.47 222	78.37 229	82.78 233	83.35 356	63.96 265	86.96 213	90.36 181	69.99 273	77.50 238	85.67 324	60.66 234	93.77 163	74.27 203	76.58 353	90.62 244

ACMM	73.20 8	80.78 189	79.84 191	83.58 191	89.31 149	68.37 135	89.99 84	91.60 140	70.28 266	77.25 244	89.66 205	53.37 304	93.53 178	74.24 204	82.85 274	88.85 318
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 232		58.10 442	87.04 62			88.98 358	74.07 205

v1192		79.59 219	78.43 228	83.07 214	83.55 353	64.52 252	86.93 216	90.58 171	70.83 246	77.78 234	85.90 317	59.15 248	93.94 149	73.96 206	77.19 345	90.76 238

v10		79.74 216	78.67 221	82.97 221	84.06 339	64.95 238	87.88 180	90.62 170	73.11 199	75.11 305	86.56 303	61.46 217	94.05 145	73.68 207	75.55 371	89.90 282

v1921920		79.22 231	78.03 236	82.80 229	83.30 358	63.94 267	86.80 221	90.33 182	69.91 276	77.48 239	85.53 328	58.44 254	93.75 165	73.60 208	76.85 350	90.71 242

cl22		78.07 262	77.01 265	81.23 274	82.37 389	61.83 321	83.55 325	87.98 275	68.96 306	75.06 307	83.87 366	61.40 219	91.88 266	73.53 209	76.39 358	89.98 279

Effi-MVS+-dtu		80.03 212	78.57 224	84.42 138	85.13 316	68.74 122	88.77 136	88.10 270	74.99 140	74.97 310	83.49 379	57.27 266	93.36 192	73.53 209	80.88 298	91.18 221

c3_l		78.75 243	77.91 239	81.26 273	82.89 377	61.56 325	84.09 312	89.13 236	69.97 274	75.56 284	84.29 356	66.36 145	92.09 256	73.47 211	75.48 373	90.12 267

VDDNet		81.52 167	80.67 167	84.05 171	90.44 109	64.13 263	89.73 93	85.91 330	71.11 237	83.18 128	93.48 79	50.54 346	93.49 183	73.40 212	88.25 168	94.54 56

CANet_DTU		80.61 192	79.87 190	82.83 226	85.60 301	63.17 293	87.36 200	88.65 262	76.37 100	75.88 279	88.44 245	53.51 302	93.07 213	73.30 213	89.74 137	92.25 184

miper_ehance_all_eth		78.59 249	77.76 248	81.08 279	82.66 382	61.56 325	83.65 320	89.15 234	68.87 307	75.55 285	83.79 370	66.49 143	92.03 257	73.25 214	76.39 358	89.64 291

3Dnovator	76.31 5	83.38 126	82.31 140	86.59 62	87.94 210	72.94 28	90.64 68	92.14 112	77.21 66	75.47 286	92.83 98	58.56 253	94.72 117	73.24 215	92.71 81	92.13 194

v1240		78.99 238	77.78 246	82.64 238	83.21 361	63.54 281	86.62 230	90.30 184	69.74 283	77.33 242	85.68 323	57.04 269	93.76 164	73.13 216	76.92 347	90.62 244

casdiffseed414692147		83.62 118	83.02 124	85.40 92	87.31 252	67.50 168	88.70 142	91.72 132	76.97 74	82.77 139	91.72 132	66.85 137	93.71 168	73.06 217	88.12 171	94.98 13

miper_enhance_ethall		77.87 269	76.86 269	80.92 284	81.65 397	61.38 329	82.68 343	88.98 242	65.52 354	75.47 286	82.30 399	65.76 157	92.00 260	72.95 218	76.39 358	89.39 298

MG-MVS		83.41 124	83.45 116	83.28 201	92.74 72	62.28 313	88.17 167	89.50 212	75.22 131	81.49 159	92.74 104	66.75 138	95.11 95	72.85 219	91.58 102	92.45 176

EPP-MVSNet		83.40 125	83.02 124	84.57 127	90.13 115	64.47 256	92.32 35	90.73 168	74.45 158	79.35 199	91.10 159	69.05 110	95.12 93	72.78 220	87.22 190	94.13 77

test_fmvs3		63.36 435	61.82 437	67.98 454	62.51 494	46.96 475	77.37 427	74.03 461	45.24 479	67.50 406	78.79 438	12.16 498	72.98 484	72.77 221	66.02 441	83.99 433

IterMVS-LS		80.06 210	79.38 205	82.11 251	85.89 293	63.20 291	86.79 222	89.34 217	74.19 166	75.45 289	86.72 292	66.62 140	92.39 244	72.58 222	76.86 349	90.75 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0805		78.73 244	77.83 243	81.43 266	85.17 312	60.30 352	89.41 107	90.90 161	71.21 235	77.17 251	88.73 234	46.38 388	93.21 201	72.57 223	78.96 324	90.79 236

EI-MVSNet		80.52 198	79.98 186	82.12 249	84.28 333	63.19 292	86.41 237	88.95 245	74.18 167	78.69 208	87.54 272	66.62 140	92.43 242	72.57 223	80.57 304	90.74 240

icg_test_0407_2		78.92 241	78.93 218	78.90 338	87.13 258	63.59 277	76.58 432	89.33 218	70.51 257	77.82 231	89.03 224	61.84 207	81.38 438	72.56 225	85.56 225	91.74 202

IMVS_0407		80.61 192	79.90 189	82.75 236	87.13 258	63.59 277	85.33 273	89.33 218	70.51 257	77.82 231	89.03 224	61.84 207	92.91 220	72.56 225	85.56 225	91.74 202

IMVS_0404		77.16 285	76.42 282	79.37 329	87.13 258	63.59 277	77.12 429	89.33 218	70.51 257	66.22 428	89.03 224	50.36 348	82.78 427	72.56 225	85.56 225	91.74 202

IMVS_0403		80.80 185	80.12 184	82.87 225	87.13 258	63.59 277	85.19 274	89.33 218	70.51 257	78.49 215	89.03 224	63.26 180	93.27 196	72.56 225	85.56 225	91.74 202

SSM_0407		81.58 164	80.48 173	84.87 117	88.81 169	67.96 150	87.37 199	89.25 228	71.06 240	79.48 195	90.39 184	59.57 244	94.48 128	72.45 229	85.93 218	92.18 189

SSM_0404		81.91 154	80.84 165	85.13 103	89.24 153	68.26 138	87.84 182	89.25 228	71.06 240	80.62 177	90.39 184	59.57 244	94.65 121	72.45 229	87.19 191	92.47 175

Vis-MVSNet		83.46 123	82.80 130	85.43 91	90.25 113	68.74 122	90.30 80	90.13 190	76.33 102	80.87 173	92.89 96	61.00 228	94.20 138	72.45 229	90.97 113	93.35 125
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LFMVS		81.82 157	81.23 157	83.57 192	91.89 83	63.43 286	89.84 87	81.85 392	77.04 73	83.21 125	93.10 89	52.26 313	93.43 190	71.98 232	89.95 133	93.85 93

v148		78.72 245	77.80 245	81.47 265	82.73 380	61.96 319	86.30 244	88.08 271	73.26 194	76.18 274	85.47 330	62.46 197	92.36 246	71.92 233	73.82 397	90.09 270

PVSNet_BlendedMVS		80.60 194	80.02 185	82.36 245	88.85 165	65.40 219	86.16 250	92.00 115	69.34 289	78.11 225	86.09 316	66.02 153	94.27 133	71.52 234	82.06 284	87.39 360

PVSNet_Blended		80.98 177	80.34 176	82.90 223	88.85 165	65.40 219	84.43 301	92.00 115	67.62 323	78.11 225	85.05 342	66.02 153	94.27 133	71.52 234	89.50 141	89.01 310

eth_miper_zixun_eth		77.92 267	76.69 276	81.61 263	83.00 370	61.98 318	83.15 335	89.20 232	69.52 286	74.86 312	84.35 355	61.76 210	92.56 235	71.50 236	72.89 405	90.28 261

UA-Net		85.08 85	84.96 85	85.45 90	92.07 80	68.07 146	89.78 91	90.86 164	82.48 2	84.60 93	93.20 88	69.35 99	95.22 89	71.39 237	90.88 117	93.07 144

FA-MVS(test-final)		80.96 178	79.91 188	84.10 159	88.30 193	65.01 235	84.55 295	90.01 193	73.25 195	79.61 192	87.57 269	58.35 255	94.72 117	71.29 238	86.25 209	92.56 167

dtuplus		80.04 211	79.40 204	81.97 255	83.08 366	62.61 303	83.63 323	87.98 275	67.47 327	81.02 168	90.50 181	64.86 165	90.77 323	71.28 239	84.76 236	92.53 169

cl____		77.72 272	76.76 273	80.58 291	82.49 386	60.48 349	83.09 338	87.87 280	69.22 294	74.38 321	85.22 337	62.10 204	91.53 284	71.09 240	75.41 377	89.73 290

DIV-MVS_self_test		77.72 272	76.76 273	80.58 291	82.48 387	60.48 349	83.09 338	87.86 281	69.22 294	74.38 321	85.24 335	62.10 204	91.53 284	71.09 240	75.40 378	89.74 289

MonoMVSNet		76.49 298	75.80 287	78.58 344	81.55 400	58.45 368	86.36 242	86.22 325	74.87 148	74.73 314	83.73 372	51.79 328	88.73 363	70.78 242	72.15 410	88.55 331

test_yl		81.17 172	80.47 174	83.24 204	89.13 158	63.62 273	86.21 248	89.95 195	72.43 211	81.78 154	89.61 207	57.50 263	93.58 170	70.75 243	86.90 196	92.52 170

DCV-MVSNet		81.17 172	80.47 174	83.24 204	89.13 158	63.62 273	86.21 248	89.95 195	72.43 211	81.78 154	89.61 207	57.50 263	93.58 170	70.75 243	86.90 196	92.52 170

VNet		82.21 148	82.41 137	81.62 261	90.82 101	60.93 338	84.47 296	89.78 199	76.36 101	84.07 106	91.88 125	64.71 166	90.26 332	70.68 245	88.89 151	93.66 105

mvs_anonymous		79.42 225	79.11 214	80.34 297	84.45 332	57.97 376	82.59 344	87.62 287	67.40 328	76.17 276	88.56 242	68.47 118	89.59 345	70.65 246	86.05 214	93.47 120

VPA-MVSNet		80.60 194	80.55 171	80.76 287	88.07 204	60.80 341	86.86 219	91.58 141	75.67 119	80.24 185	89.45 216	63.34 177	90.25 333	70.51 247	79.22 323	91.23 220

PAPM_NR		83.02 136	82.41 137	84.82 119	92.47 77	66.37 194	87.93 177	91.80 127	73.82 175	77.32 243	90.66 174	67.90 126	94.90 105	70.37 248	89.48 142	93.19 135

mamba_0408		79.37 229	77.52 255	84.93 113	88.81 169	67.96 150	65.03 486	88.66 260	70.96 244	79.48 195	89.80 199	58.69 250	94.65 121	70.35 249	85.93 218	92.18 189

SSM_04072		77.67 276	77.52 255	78.12 355	88.81 169	67.96 150	65.03 486	88.66 260	70.96 244	79.48 195	89.80 199	58.69 250	74.23 479	70.35 249	85.93 218	92.18 189

thisisatest0530		79.40 226	77.76 248	84.31 146	87.69 230	65.10 234	87.36 200	84.26 353	70.04 270	77.42 240	88.26 251	49.94 354	94.79 114	70.20 251	84.70 238	93.03 148

tttt0517		79.40 226	77.91 239	83.90 182	88.10 202	63.84 269	88.37 159	84.05 355	71.45 229	76.78 257	89.12 221	49.93 356	94.89 107	70.18 252	83.18 271	92.96 153

UniMVSNet_NR-MVSNet		81.88 155	81.54 154	82.92 222	88.46 186	63.46 284	87.13 206	92.37 88	80.19 12	78.38 218	89.14 220	71.66 66	93.05 215	70.05 253	76.46 356	92.25 184

DU-MVS		81.12 175	80.52 172	82.90 223	87.80 217	63.46 284	87.02 211	91.87 123	79.01 31	78.38 218	89.07 222	65.02 162	93.05 215	70.05 253	76.46 356	92.20 187

XVG-ACMP-BASELINE		76.11 306	74.27 318	81.62 261	83.20 362	64.67 249	83.60 324	89.75 203	69.75 281	71.85 353	87.09 285	32.78 466	92.11 255	69.99 255	80.43 306	88.09 342

GeoE		81.71 159	81.01 162	83.80 186	89.51 136	64.45 257	88.97 126	88.73 258	71.27 234	78.63 211	89.76 202	66.32 146	93.20 204	69.89 256	86.02 215	93.74 102

FIs		82.07 151	82.42 136	81.04 280	88.80 173	58.34 370	88.26 164	93.49 31	76.93 76	78.47 217	91.04 162	69.92 91	92.34 248	69.87 257	84.97 232	92.44 177

114514_t		80.68 190	79.51 201	84.20 156	94.09 42	67.27 178	89.64 96	91.11 156	58.75 437	74.08 323	90.72 171	58.10 256	95.04 100	69.70 258	89.42 143	90.30 260

Anonymous20231211		78.97 239	77.69 251	82.81 228	90.54 107	64.29 260	90.11 83	91.51 143	65.01 366	76.16 277	88.13 258	50.56 345	93.03 218	69.68 259	77.56 342	91.11 223

Patchmatch-RL test		70.24 386	67.78 400	77.61 367	77.43 452	59.57 361	71.16 461	70.33 469	62.94 393	68.65 389	72.77 472	50.62 344	85.49 403	69.58 260	66.58 439	87.77 349

UniMVSNet (Re)		81.60 163	81.11 159	83.09 211	88.38 190	64.41 258	87.60 186	93.02 51	78.42 37	78.56 213	88.16 253	69.78 93	93.26 197	69.58 260	76.49 355	91.60 207

IterMVS-SCA-FT		75.43 316	73.87 323	80.11 305	82.69 381	64.85 246	81.57 361	83.47 364	69.16 297	70.49 365	84.15 364	51.95 321	88.15 372	69.23 262	72.14 411	87.34 365

v7n		78.97 239	77.58 254	83.14 209	83.45 355	65.51 217	88.32 161	91.21 151	73.69 179	72.41 346	86.32 310	57.93 257	93.81 160	69.18 263	75.65 369	90.11 268

Anonymous20240529		80.19 209	78.89 219	84.10 159	90.60 105	64.75 248	88.95 127	90.90 161	65.97 349	80.59 178	91.17 158	49.97 353	93.73 167	69.16 264	82.70 278	93.81 97

miper_lstm_enhance		74.11 331	73.11 333	77.13 375	80.11 420	59.62 359	72.23 457	86.92 311	66.76 333	70.40 366	82.92 389	56.93 270	82.92 426	69.06 265	72.63 406	88.87 317

testdata				79.97 309	90.90 99	64.21 261		84.71 344	59.27 430	85.40 76	92.91 95	62.02 206	89.08 356	68.95 266	91.37 106	86.63 390

test1111		79.43 224	79.18 213	80.15 304	89.99 122	53.31 439	87.33 202	77.05 445	75.04 139	80.23 186	92.77 103	48.97 370	92.33 249	68.87 267	92.40 86	94.81 26

GA-MVS		76.87 290	75.17 305	81.97 255	82.75 379	62.58 304	81.44 364	86.35 324	72.16 216	74.74 313	82.89 390	46.20 393	92.02 259	68.85 268	81.09 295	91.30 219

test2506		77.30 283	76.49 279	79.74 319	90.08 117	52.02 445	87.86 181	63.10 489	74.88 146	80.16 187	92.79 101	38.29 450	92.35 247	68.74 269	92.50 84	94.86 21

ECVR-MVS		79.61 217	79.26 210	80.67 289	90.08 117	54.69 426	87.89 179	77.44 441	74.88 146	80.27 184	92.79 101	48.96 371	92.45 241	68.55 270	92.50 84	94.86 21

UGNet		80.83 181	79.59 200	84.54 128	88.04 205	68.09 145	89.42 106	88.16 268	76.95 75	76.22 272	89.46 214	49.30 365	93.94 149	68.48 271	90.31 124	91.60 207
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
FC-MVSNet-test		81.52 167	82.02 148	80.03 306	88.42 189	55.97 410	87.95 175	93.42 34	77.10 71	77.38 241	90.98 167	69.96 90	91.79 268	68.46 272	84.50 240	92.33 180

DP-MVS Recon		83.11 135	82.09 146	86.15 71	94.44 23	70.92 78	88.79 135	92.20 105	70.53 256	79.17 201	91.03 164	64.12 172	96.03 56	68.39 273	90.14 128	91.50 212

UniMVSNet_ETH3D		79.10 235	78.24 233	81.70 260	86.85 269	60.24 353	87.28 204	88.79 250	74.25 165	76.84 254	90.53 180	49.48 360	91.56 279	67.98 274	82.15 282	93.29 127

D2MVS		74.82 323	73.21 331	79.64 324	79.81 425	62.56 306	80.34 384	87.35 294	64.37 374	68.86 387	82.66 394	46.37 389	90.10 335	67.91 275	81.24 293	86.25 393

IS-MVSNet		83.15 132	82.81 129	84.18 157	89.94 124	63.30 288	91.59 51	88.46 266	79.04 30	79.49 194	92.16 117	65.10 161	94.28 132	67.71 276	91.86 98	94.95 14

Fast-Effi-MVS+-dtu		78.02 264	76.49 279	82.62 239	83.16 365	66.96 187	86.94 215	87.45 292	72.45 208	71.49 358	84.17 363	54.79 289	91.58 276	67.61 277	80.31 307	89.30 301

PAPR		81.66 162	80.89 164	83.99 178	90.27 112	64.00 264	86.76 225	91.77 130	68.84 308	77.13 253	89.50 210	67.63 128	94.88 108	67.55 278	88.52 160	93.09 143

cascas		76.72 292	74.64 310	82.99 218	85.78 296	65.88 206	82.33 348	89.21 231	60.85 415	72.74 340	81.02 411	47.28 378	93.75 165	67.48 279	85.02 231	89.34 300

1314		76.53 294	75.30 303	80.21 302	83.93 342	62.32 312	84.66 290	88.81 249	60.23 420	70.16 371	84.07 365	55.30 283	90.73 325	67.37 280	83.21 270	87.59 354

无先验							87.48 189	88.98 242	60.00 423				94.12 142	67.28 281		88.97 313

thisisatest0515		77.33 282	75.38 298	83.18 207	85.27 311	63.80 270	82.11 352	83.27 367	65.06 364	75.91 278	83.84 368	49.54 359	94.27 133	67.24 282	86.19 210	91.48 214

原ACMM1				84.35 143	93.01 66	68.79 118		92.44 83	63.96 382	81.09 166	91.57 142	66.06 152	95.45 76	67.19 283	94.82 49	88.81 320

Baseline_NR-MVSNet		78.15 260	78.33 231	77.61 367	85.79 295	56.21 408	86.78 223	85.76 333	73.60 182	77.93 230	87.57 269	65.02 162	88.99 357	67.14 284	75.33 380	87.63 351

TranMVSNet+NR-MVSNet		80.84 180	80.31 177	82.42 243	87.85 214	62.33 311	87.74 184	91.33 148	80.55 9	77.99 229	89.86 195	65.23 160	92.62 230	67.05 285	75.24 383	92.30 182

Fast-Effi-MVS+		80.81 182	79.92 187	83.47 193	88.85 165	64.51 253	85.53 269	89.39 216	70.79 247	78.49 215	85.06 341	67.54 129	93.58 170	67.03 286	86.58 202	92.32 181

VPNet		78.69 246	78.66 222	78.76 340	88.31 192	55.72 414	84.45 299	86.63 318	76.79 80	78.26 221	90.55 179	59.30 247	89.70 344	66.63 287	77.05 346	90.88 233

PM-MVS		66.41 422	64.14 425	73.20 418	73.92 468	56.45 401	78.97 405	64.96 486	63.88 383	64.72 439	80.24 422	19.84 490	83.44 423	66.24 288	64.52 453	79.71 466

test-LLR		72.94 355	72.43 340	74.48 402	81.35 405	58.04 374	78.38 413	77.46 439	66.66 335	69.95 375	79.00 435	48.06 374	79.24 446	66.13 289	84.83 234	86.15 396

test-mter		71.41 371	70.39 372	74.48 402	81.35 405	58.04 374	78.38 413	77.46 439	60.32 419	69.95 375	79.00 435	36.08 460	79.24 446	66.13 289	84.83 234	86.15 396

MVS		78.19 259	76.99 267	81.78 258	85.66 298	66.99 184	84.66 290	90.47 175	55.08 460	72.02 352	85.27 334	63.83 175	94.11 143	66.10 291	89.80 136	84.24 429

NR-MVSNet		80.23 207	79.38 205	82.78 233	87.80 217	63.34 287	86.31 243	91.09 157	79.01 31	72.17 350	89.07 222	67.20 133	92.81 227	66.08 292	75.65 369	92.20 187

CVMVSNet		72.99 354	72.58 339	74.25 406	84.28 333	50.85 459	86.41 237	83.45 365	44.56 480	73.23 334	87.54 272	49.38 362	85.70 399	65.90 293	78.44 329	86.19 395

IterMVS		74.29 327	72.94 335	78.35 351	81.53 401	63.49 283	81.58 360	82.49 382	68.06 320	69.99 374	83.69 374	51.66 330	85.54 402	65.85 294	71.64 414	86.01 400
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 328	72.42 341	79.80 314	83.76 347	59.59 360	85.92 256	86.64 317	66.39 342	66.96 415	87.58 268	39.46 441	91.60 275	65.76 295	69.27 425	88.22 339

tpmrst		72.39 360	72.13 344	73.18 419	80.54 414	49.91 463	79.91 392	79.08 429	63.11 389	71.69 355	79.95 425	55.32 282	82.77 428	65.66 296	73.89 395	86.87 381

MAR-MVS		81.84 156	80.70 166	85.27 96	91.32 90	71.53 59	89.82 88	90.92 160	69.77 280	78.50 214	86.21 312	62.36 199	94.52 125	65.36 297	92.05 93	89.77 288
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
Anonymous202405211		78.25 255	77.01 265	81.99 254	91.03 95	60.67 345	84.77 287	83.90 357	70.65 255	80.00 188	91.20 156	41.08 432	91.43 291	65.21 298	85.26 230	93.85 93

ab-mvs		79.51 220	78.97 217	81.14 277	88.46 186	60.91 339	83.84 315	89.24 230	70.36 262	79.03 202	88.87 232	63.23 182	90.21 334	65.12 299	82.57 279	92.28 183

IB-MVS	68.01 15	75.85 310	73.36 330	83.31 200	84.76 324	66.03 199	83.38 330	85.06 341	70.21 269	69.40 381	81.05 410	45.76 398	94.66 120	65.10 300	75.49 372	89.25 302
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
WR-MVS		79.49 221	79.22 212	80.27 299	88.79 174	58.35 369	85.06 281	88.61 264	78.56 35	77.65 236	88.34 247	63.81 176	90.66 326	64.98 301	77.22 344	91.80 201

CostFormer		75.24 320	73.90 322	79.27 331	82.65 383	58.27 371	80.80 372	82.73 381	61.57 410	75.33 298	83.13 385	55.52 281	91.07 308	64.98 301	78.34 334	88.45 332

API-MVS		81.99 153	81.23 157	84.26 154	90.94 98	70.18 92	91.10 63	89.32 222	71.51 228	78.66 210	88.28 249	65.26 159	95.10 98	64.74 303	91.23 109	87.51 357

新几何1				83.42 196	93.13 60	70.71 81		85.48 336	57.43 449	81.80 153	91.98 122	63.28 178	92.27 250	64.60 304	92.99 76	87.27 368

testing91		76.54 293	75.66 292	79.18 334	88.43 188	55.89 411	81.08 369	83.00 375	73.76 177	75.34 294	84.29 356	46.20 393	90.07 336	64.33 305	84.50 240	91.58 209

testing99		76.09 307	75.12 306	79.00 335	88.16 197	55.50 417	80.79 373	81.40 397	73.30 193	75.17 302	84.27 359	44.48 408	90.02 337	64.28 306	84.22 249	91.48 214

pm-mvs1		77.25 284	76.68 277	78.93 337	84.22 335	58.62 367	86.41 237	88.36 267	71.37 230	73.31 332	88.01 259	61.22 224	89.15 355	64.24 307	73.01 404	89.03 309

TESTMET0.1,1		69.89 394	69.00 383	72.55 424	79.27 435	56.85 394	78.38 413	74.71 459	57.64 445	68.09 397	77.19 450	37.75 452	76.70 459	63.92 308	84.09 250	84.10 432

QAPM		80.88 179	79.50 202	85.03 106	88.01 208	68.97 115	91.59 51	92.00 115	66.63 340	75.15 304	92.16 117	57.70 260	95.45 76	63.52 309	88.76 155	90.66 243

baseline2		75.70 311	73.83 324	81.30 271	83.26 359	61.79 322	82.57 345	80.65 405	66.81 331	66.88 416	83.42 380	57.86 259	92.19 253	63.47 310	79.57 314	89.91 281

LCM-MVSNet-Re		77.05 286	76.94 268	77.36 371	87.20 255	51.60 452	80.06 388	80.46 410	75.20 134	67.69 404	86.72 292	62.48 196	88.98 358	63.44 311	89.25 144	91.51 211

gm-plane-assit					81.40 403	53.83 434			62.72 398		80.94 413		92.39 244	63.40 312

baseline1		76.98 288	76.75 275	77.66 365	88.13 200	55.66 415	85.12 278	81.89 390	73.04 201	76.79 256	88.90 230	62.43 198	87.78 378	63.30 313	71.18 417	89.55 294

blended_shiyan8		73.38 341	71.17 357	80.02 307	78.36 440	61.51 327	82.43 346	87.28 295	65.40 358	68.61 390	77.53 448	51.91 324	91.00 313	63.28 314	65.76 444	87.53 356

blended_shiyan6		73.38 341	71.17 357	80.01 308	78.36 440	61.48 328	82.43 346	87.27 298	65.40 358	68.56 392	77.55 447	51.94 323	91.01 310	63.27 315	65.76 444	87.55 355

usedtu_blend_shiyan5		73.29 347	70.96 361	80.25 300	77.80 447	62.16 315	84.44 300	87.38 293	64.41 372	68.09 397	76.28 457	51.32 332	91.23 298	63.21 316	65.76 444	87.35 362

blend_shiyan4		72.29 364	69.65 377	80.21 302	78.24 443	62.16 315	82.29 349	87.27 298	65.41 357	68.43 396	76.42 456	39.91 439	91.23 298	63.21 316	65.66 449	87.22 369

wanda-best-256-512		72.94 355	70.66 365	79.79 315	77.80 447	61.03 336	81.31 366	87.15 303	65.18 361	68.09 397	76.28 457	51.32 332	90.97 314	63.06 318	65.76 444	87.35 362

FE-blended-shiyan7		72.94 355	70.66 365	79.79 315	77.80 447	61.03 336	81.31 366	87.15 303	65.18 361	68.09 397	76.28 457	51.32 332	90.97 314	63.06 318	65.76 444	87.35 362

AdaColmap		80.58 197	79.42 203	84.06 168	93.09 63	68.91 116	89.36 110	88.97 244	69.27 291	75.70 282	89.69 203	57.20 268	95.77 65	63.06 318	88.41 163	87.50 358

test_vis1_rt		60.28 440	58.42 443	65.84 459	67.25 488	55.60 416	70.44 466	60.94 492	44.33 481	59.00 466	66.64 482	24.91 481	68.67 490	62.80 321	69.48 423	73.25 478

gbinet_0.2-2-1-0.02		73.24 349	70.86 364	80.39 294	78.03 445	61.62 324	83.10 337	86.69 314	65.98 348	69.29 384	76.15 460	49.77 357	91.51 286	62.75 322	66.00 442	88.03 343

GBi-Net		78.40 252	77.40 258	81.40 268	87.60 233	63.01 294	88.39 156	89.28 224	71.63 223	75.34 294	87.28 276	54.80 286	91.11 302	62.72 323	79.57 314	90.09 270

test1		78.40 252	77.40 258	81.40 268	87.60 233	63.01 294	88.39 156	89.28 224	71.63 223	75.34 294	87.28 276	54.80 286	91.11 302	62.72 323	79.57 314	90.09 270

FMVSNet3		77.88 268	76.85 270	80.97 283	86.84 270	62.36 310	86.52 234	88.77 251	71.13 236	75.34 294	86.66 298	54.07 296	91.10 305	62.72 323	79.57 314	89.45 296

CMPMVS	51.72 21	70.19 387	68.16 389	76.28 380	73.15 476	57.55 386	79.47 396	83.92 356	48.02 476	56.48 475	84.81 346	43.13 417	86.42 392	62.67 326	81.81 288	84.89 422
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset		77.70 274	77.40 258	78.60 343	89.03 163	60.02 355	79.00 404	85.83 332	75.19 135	76.61 263	89.98 193	54.81 285	85.46 404	62.63 327	83.55 262	90.33 258

0.4-1-1-0.1		70.93 376	67.94 395	79.91 310	79.35 433	61.27 330	78.95 406	82.19 387	63.36 386	67.50 406	69.40 479	39.83 440	91.04 309	62.44 328	68.40 431	87.40 359

usedtu_dtu_shiyan1		76.43 299	75.32 301	79.76 317	83.00 370	60.72 342	81.74 356	88.76 255	68.99 304	72.98 337	84.19 361	56.41 276	90.27 330	62.39 329	79.40 318	88.31 335

FE-MVSNET3		76.43 299	75.32 301	79.76 317	83.00 370	60.72 342	81.74 356	88.76 255	68.99 304	72.98 337	84.19 361	56.41 276	90.27 330	62.39 329	79.40 318	88.31 335

FMVSNet2		78.20 258	77.21 262	81.20 275	87.60 233	62.89 301	87.47 190	89.02 240	71.63 223	75.29 300	87.28 276	54.80 286	91.10 305	62.38 331	79.38 320	89.61 292

testdata2													91.01 310	62.37 332

testing11		75.14 321	74.01 319	78.53 347	88.16 197	56.38 404	80.74 376	80.42 412	70.67 251	72.69 343	83.72 373	43.61 415	89.86 339	62.29 333	83.76 255	89.36 299

CP-MVSNet		78.22 256	78.34 230	77.84 361	87.83 216	54.54 428	87.94 176	91.17 153	77.65 47	73.48 331	88.49 243	62.24 202	88.43 369	62.19 334	74.07 392	90.55 248

XXY-MVS		75.41 317	75.56 293	74.96 396	83.59 352	57.82 380	80.59 379	83.87 358	66.54 341	74.93 311	88.31 248	63.24 181	80.09 444	62.16 335	76.85 350	86.97 380

pmmvs6		74.69 324	73.39 328	78.61 342	81.38 404	57.48 387	86.64 229	87.95 278	64.99 367	70.18 369	86.61 299	50.43 347	89.52 346	62.12 336	70.18 422	88.83 319

1112_ss		77.40 281	76.43 281	80.32 298	89.11 162	60.41 351	83.65 320	87.72 286	62.13 406	73.05 336	86.72 292	62.58 195	89.97 338	62.11 337	80.80 300	90.59 247

0.3-1-1-0.015		70.03 390	66.80 414	79.72 320	78.18 444	61.07 334	77.63 424	82.32 386	62.65 399	65.50 432	67.29 480	37.62 454	90.91 316	61.99 338	68.04 433	87.19 371

PS-CasMVS		78.01 265	78.09 235	77.77 363	87.71 226	54.39 430	88.02 172	91.22 150	77.50 55	73.26 333	88.64 238	60.73 230	88.41 370	61.88 339	73.88 396	90.53 249

CDS-MVSNet		79.07 236	77.70 250	83.17 208	87.60 233	68.23 142	84.40 304	86.20 326	67.49 325	76.36 269	86.54 304	61.54 214	90.79 320	61.86 340	87.33 188	90.49 251
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	72.83 10	79.77 215	78.33 231	84.09 163	85.17 312	69.91 94	90.57 69	90.97 159	66.70 334	72.17 350	91.91 123	54.70 290	93.96 146	61.81 341	90.95 115	88.41 334

0.4-1-1-0.2		70.01 391	66.86 413	79.44 328	77.61 450	60.64 346	76.77 431	82.34 385	62.40 402	65.91 430	66.65 481	40.05 437	90.83 318	61.77 342	68.24 432	86.86 382

K. test v3		71.19 372	68.51 385	79.21 333	83.04 369	57.78 382	84.35 305	76.91 446	72.90 204	62.99 451	82.86 391	39.27 442	91.09 307	61.65 343	52.66 480	88.75 323

CHOSEN 1792x2688		77.63 277	75.69 289	83.44 195	89.98 123	68.58 130	78.70 409	87.50 290	56.38 454	75.80 281	86.84 288	58.67 252	91.40 292	61.58 344	85.75 223	90.34 257

dtuonly		69.95 392	69.98 375	69.85 442	73.09 477	49.46 466	74.55 450	76.40 449	57.56 448	67.82 401	86.31 311	50.89 343	74.23 479	61.46 345	81.71 289	85.86 406

PCF-MVS	73.52 7	80.38 201	78.84 220	85.01 108	87.71 226	68.99 114	83.65 320	91.46 147	63.00 391	77.77 235	90.28 187	66.10 150	95.09 99	61.40 346	88.22 169	90.94 232
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	69.67 12	77.95 266	77.15 263	80.36 296	87.57 242	60.21 354	83.37 331	87.78 284	66.11 344	75.37 293	87.06 287	63.27 179	90.48 328	61.38 347	82.43 280	90.40 255

HyFIR lowres test		77.53 278	75.40 297	83.94 181	89.59 132	66.62 190	80.36 383	88.64 263	56.29 455	76.45 266	85.17 338	57.64 261	93.28 194	61.34 348	83.10 272	91.91 198

PMMVS		69.34 398	68.67 384	71.35 434	75.67 460	62.03 317	75.17 442	73.46 462	50.00 473	68.68 388	79.05 433	52.07 319	78.13 451	61.16 349	82.77 275	73.90 477

FMVSNet1		77.44 279	76.12 286	81.40 268	86.81 271	63.01 294	88.39 156	89.28 224	70.49 261	74.39 320	87.28 276	49.06 369	91.11 302	60.91 350	78.52 327	90.09 270

sss		73.60 338	73.64 326	73.51 414	82.80 378	55.01 423	76.12 434	81.69 393	62.47 401	74.68 315	85.85 320	57.32 265	78.11 452	60.86 351	80.93 296	87.39 360

Test_1112_low_res		76.40 302	75.44 295	79.27 331	89.28 151	58.09 372	81.69 359	87.07 306	59.53 428	72.48 345	86.67 297	61.30 221	89.33 349	60.81 352	80.15 309	90.41 254

sc_t1		72.19 366	69.51 378	80.23 301	84.81 322	61.09 333	84.68 289	80.22 417	60.70 416	71.27 359	83.58 377	36.59 457	89.24 352	60.41 353	63.31 456	90.37 256

BH-untuned		79.47 222	78.60 223	82.05 252	89.19 156	65.91 205	86.07 252	88.52 265	72.18 214	75.42 290	87.69 266	61.15 225	93.54 177	60.38 354	86.83 199	86.70 387

WTY-MVS		75.65 312	75.68 290	75.57 387	86.40 283	56.82 395	77.92 422	82.40 383	65.10 363	76.18 274	87.72 264	63.13 187	80.90 441	60.31 355	81.96 285	89.00 312

pmmvs4		74.03 334	71.91 345	80.39 294	81.96 393	68.32 136	81.45 363	82.14 388	59.32 429	69.87 377	85.13 339	52.40 311	88.13 373	60.21 356	74.74 388	84.73 425

PEN-MVS		77.73 271	77.69 251	77.84 361	87.07 266	53.91 433	87.91 178	91.18 152	77.56 52	73.14 335	88.82 233	61.23 223	89.17 354	59.95 357	72.37 407	90.43 253

CR-MVSNet		73.37 343	71.27 355	79.67 323	81.32 407	65.19 229	75.92 436	80.30 415	59.92 424	72.73 341	81.19 408	52.50 309	86.69 387	59.84 358	77.71 338	87.11 376

mvs5depth		69.45 397	67.45 406	75.46 391	73.93 467	55.83 412	79.19 401	83.23 368	66.89 330	71.63 356	83.32 381	33.69 465	85.09 407	59.81 359	55.34 477	85.46 411

lessismore_v0				78.97 336	81.01 410	57.15 391		65.99 482		61.16 458	82.82 392	39.12 444	91.34 294	59.67 360	46.92 487	88.43 333

CNLPA		78.08 261	76.79 272	81.97 255	90.40 110	71.07 72	87.59 187	84.55 347	66.03 347	72.38 347	89.64 206	57.56 262	86.04 396	59.61 361	83.35 267	88.79 321

BH-RMVSNet		79.61 217	78.44 227	83.14 209	89.38 145	65.93 204	84.95 284	87.15 303	73.56 183	78.19 223	89.79 201	56.67 273	93.36 192	59.53 362	86.74 200	90.13 266

FE-MVSNET2		72.88 358	71.28 354	77.67 364	78.30 442	57.78 382	84.43 301	88.92 247	69.56 284	64.61 440	81.67 406	46.73 386	88.54 368	59.33 363	67.99 434	86.69 388

MS-PatchMatch		73.83 335	72.67 337	77.30 373	83.87 344	66.02 200	81.82 354	84.66 345	61.37 413	68.61 390	82.82 392	47.29 377	88.21 371	59.27 364	84.32 247	77.68 471

test_post1							78.90 408				5.43 523	48.81 373	85.44 405	59.25 365

SCA		74.22 329	72.33 342	79.91 310	84.05 340	62.17 314	79.96 391	79.29 427	66.30 343	72.38 347	80.13 423	51.95 321	88.60 366	59.25 365	77.67 341	88.96 314

FE-MVS		77.78 270	75.68 290	84.08 164	88.09 203	66.00 202	83.13 336	87.79 283	68.42 316	78.01 228	85.23 336	45.50 402	95.12 93	59.11 367	85.83 222	91.11 223

SixPastTwentyTwo		73.37 343	71.26 356	79.70 321	85.08 317	57.89 378	85.57 263	83.56 362	71.03 242	65.66 431	85.88 318	42.10 425	92.57 234	59.11 367	63.34 455	88.65 327

WR-MVS_H		78.51 251	78.49 225	78.56 345	88.02 206	56.38 404	88.43 153	92.67 73	77.14 68	73.89 325	87.55 271	66.25 147	89.24 352	58.92 369	73.55 399	90.06 274

PLC	70.83 11	78.05 263	76.37 284	83.08 213	91.88 84	67.80 157	88.19 166	89.46 213	64.33 375	69.87 377	88.38 246	53.66 300	93.58 170	58.86 370	82.73 276	87.86 347
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 350	71.46 350	78.54 346	82.50 385	59.85 356	82.18 351	82.84 380	58.96 433	71.15 362	89.41 218	45.48 403	84.77 411	58.82 371	71.83 413	91.02 229

EU-MVSNet		68.53 406	67.61 403	71.31 435	78.51 439	47.01 474	84.47 296	84.27 352	42.27 483	66.44 426	84.79 347	40.44 435	83.76 417	58.76 372	68.54 430	83.17 440

pmmvs-eth3d		70.50 383	67.83 398	78.52 348	77.37 453	66.18 197	81.82 354	81.51 395	58.90 434	63.90 447	80.42 418	42.69 420	86.28 393	58.56 373	65.30 451	83.11 442

TAMVS		78.89 242	77.51 257	83.03 216	87.80 217	67.79 158	84.72 288	85.05 342	67.63 322	76.75 258	87.70 265	62.25 201	90.82 319	58.53 374	87.13 193	90.49 251

WBMVS		73.43 340	72.81 336	75.28 393	87.91 211	50.99 458	78.59 412	81.31 399	65.51 356	74.47 319	84.83 345	46.39 387	86.68 388	58.41 375	77.86 336	88.17 341

ACMH+	68.96 14	76.01 308	74.01 319	82.03 253	88.60 181	65.31 227	88.86 130	87.55 288	70.25 268	67.75 403	87.47 274	41.27 430	93.19 206	58.37 376	75.94 366	87.60 352

tpm		72.37 362	71.71 347	74.35 404	82.19 390	52.00 446	79.22 400	77.29 443	64.56 370	72.95 339	83.68 375	51.35 331	83.26 425	58.33 377	75.80 367	87.81 348

BH-w/o		78.21 257	77.33 261	80.84 285	88.81 169	65.13 231	84.87 285	87.85 282	69.75 281	74.52 318	84.74 348	61.34 220	93.11 211	58.24 378	85.84 221	84.27 428

Vis-MVSNet (Re-imp)		78.36 254	78.45 226	78.07 357	88.64 180	51.78 451	86.70 226	79.63 423	74.14 168	75.11 305	90.83 169	61.29 222	89.75 342	58.10 379	91.60 100	92.69 163

MVP-Stereo		76.12 305	74.46 315	81.13 278	85.37 308	69.79 96	84.42 303	87.95 278	65.03 365	67.46 408	85.33 333	53.28 305	91.73 272	58.01 380	83.27 269	81.85 455
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 394	73.16 475	50.51 461	63.05 491	87.47 291		64.28 442	77.81 445	17.80 492	89.73 343	57.88 381	60.64 466	85.49 410

TR-MVS		77.44 279	76.18 285	81.20 275	88.24 194	63.24 289	84.61 293	86.40 322	67.55 324	77.81 233	86.48 306	54.10 295	93.15 208	57.75 382	82.72 277	87.20 370

F-COLMAP		76.38 303	74.33 317	82.50 242	89.28 151	66.95 188	88.41 155	89.03 239	64.05 379	66.83 417	88.61 239	46.78 384	92.89 221	57.48 383	78.55 326	87.67 350

EG-PatchMatch MVS		74.04 332	71.82 346	80.71 288	84.92 320	67.42 170	85.86 258	88.08 271	66.04 346	64.22 443	83.85 367	35.10 462	92.56 235	57.44 384	80.83 299	82.16 453

PatchmatchNet		73.12 351	71.33 353	78.49 349	83.18 363	60.85 340	79.63 394	78.57 432	64.13 376	71.73 354	79.81 428	51.20 337	85.97 397	57.40 385	76.36 363	88.66 326
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		76.99 287	76.80 271	77.54 370	86.24 285	53.06 443	87.52 188	90.66 169	77.08 72	72.50 344	88.67 237	60.48 238	89.52 346	57.33 386	70.74 419	90.05 275

UnsupCasMVSNet_eth		67.33 414	65.99 418	71.37 432	73.48 472	51.47 454	75.16 443	85.19 338	65.20 360	60.78 459	80.93 415	42.35 421	77.20 456	57.12 387	53.69 479	85.44 412

pmmvs5		71.55 370	70.20 374	75.61 386	77.83 446	56.39 403	81.74 356	80.89 401	57.76 444	67.46 408	84.49 349	49.26 366	85.32 406	57.08 388	75.29 381	85.11 419

testing3-2		75.12 322	75.19 304	74.91 397	90.40 110	45.09 482	80.29 385	78.42 433	78.37 40	76.54 265	87.75 263	44.36 409	87.28 384	57.04 389	83.49 264	92.37 178

Anonymous20240521		68.80 402	67.22 410	73.55 413	74.33 465	54.11 431	83.18 334	85.61 334	58.15 440	61.68 456	80.94 413	30.71 472	81.27 439	57.00 390	73.34 403	85.28 414

mvsany_test1		62.30 437	61.26 441	65.41 460	69.52 484	54.86 425	66.86 478	49.78 500	46.65 477	68.50 394	83.21 383	49.15 367	66.28 492	56.93 391	60.77 465	75.11 476

TransMVSNet (Re)		75.39 319	74.56 312	77.86 360	85.50 305	57.10 392	86.78 223	86.09 329	72.17 215	71.53 357	87.34 275	63.01 188	89.31 350	56.84 392	61.83 461	87.17 372

tt0320-xc		70.11 388	67.45 406	78.07 357	85.33 309	59.51 362	83.28 332	78.96 430	58.77 435	67.10 414	80.28 421	36.73 456	87.42 382	56.83 393	59.77 469	87.29 367

test_vis3_rt		49.26 457	47.02 459	56.00 471	54.30 500	45.27 481	66.76 480	48.08 501	36.83 490	44.38 489	53.20 497	7.17 505	64.07 494	56.77 394	55.66 474	58.65 490

EPMVS		69.02 400	68.16 389	71.59 430	79.61 429	49.80 465	77.40 426	66.93 480	62.82 396	70.01 372	79.05 433	45.79 397	77.86 454	56.58 395	75.26 382	87.13 375

KD-MVS_self_test		68.81 401	67.59 404	72.46 425	74.29 466	45.45 477	77.93 421	87.00 307	63.12 388	63.99 446	78.99 437	42.32 422	84.77 411	56.55 396	64.09 454	87.16 374

tpm2		73.26 348	71.46 350	78.63 341	83.34 357	56.71 398	80.65 378	80.40 413	56.63 453	73.55 330	82.02 404	51.80 327	91.24 297	56.35 397	78.42 332	87.95 344

LTVRE_ROB	69.57 13	76.25 304	74.54 313	81.41 267	88.60 181	64.38 259	79.24 399	89.12 237	70.76 249	69.79 379	87.86 262	49.09 368	93.20 204	56.21 398	80.16 308	86.65 389
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
ACMH	67.68 16	75.89 309	73.93 321	81.77 259	88.71 178	66.61 191	88.62 146	89.01 241	69.81 277	66.78 418	86.70 296	41.95 427	91.51 286	55.64 399	78.14 335	87.17 372
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		66.51 421	64.71 423	71.90 428	81.45 402	63.52 282	57.98 493	68.95 476	53.57 463	62.59 453	76.70 451	46.22 392	75.29 475	55.25 400	79.68 313	76.88 473

tt0320		70.49 384	68.03 392	77.89 359	84.78 323	59.12 364	83.55 325	80.44 411	58.13 441	67.43 410	80.41 419	39.26 443	87.54 381	55.12 401	63.18 457	86.99 379

dtuonlycased		68.45 408	67.29 409	71.92 427	80.18 419	54.90 424	79.76 393	80.38 414	60.11 422	62.57 454	76.44 455	49.34 363	82.31 430	55.05 402	61.77 462	78.53 469

UBG		73.08 352	72.27 343	75.51 389	88.02 206	51.29 456	78.35 416	77.38 442	65.52 354	73.87 326	82.36 397	45.55 400	86.48 391	55.02 403	84.39 246	88.75 323

EPNet_dtu		75.46 315	74.86 307	77.23 374	82.57 384	54.60 427	86.89 217	83.09 372	71.64 222	66.25 427	85.86 319	55.99 278	88.04 374	54.92 404	86.55 203	89.05 308
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test3		53.99 448	51.45 453	61.61 465	55.51 499	44.74 484	63.52 489	45.41 504	43.69 482	58.11 470	76.45 453	17.99 491	63.76 495	54.77 405	47.59 486	76.34 474

PVSNet	64.34 18	72.08 368	70.87 363	75.69 385	86.21 286	56.44 402	74.37 451	80.73 404	62.06 407	70.17 370	82.23 401	42.86 419	83.31 424	54.77 405	84.45 244	87.32 366

ITE_SJBPF				78.22 352	81.77 396	60.57 347		83.30 366	69.25 293	67.54 405	87.20 281	36.33 459	87.28 384	54.34 407	74.62 389	86.80 384

SSC-MVS3.2		73.35 346	73.39 328	73.23 415	85.30 310	49.01 467	74.58 449	81.57 394	75.21 133	73.68 328	85.58 327	52.53 307	82.05 433	54.33 408	77.69 340	88.63 328

MDTV_nov1_ep13_2view						37.79 496	75.16 443		55.10 459	66.53 422		49.34 363		53.98 409		87.94 345

gg-mvs-nofinetune		69.95 392	67.96 393	75.94 382	83.07 367	54.51 429	77.23 428	70.29 470	63.11 389	70.32 367	62.33 484	43.62 414	88.69 364	53.88 410	87.76 181	84.62 426

PatchMatch-RL		72.38 361	70.90 362	76.80 378	88.60 181	67.38 173	79.53 395	76.17 452	62.75 397	69.36 382	82.00 405	45.51 401	84.89 410	53.62 411	80.58 303	78.12 470

test_f		52.09 453	50.82 454	55.90 472	53.82 502	42.31 491	59.42 492	58.31 496	36.45 491	56.12 478	70.96 476	12.18 497	57.79 498	53.51 412	56.57 473	67.60 483

Patchmtry		70.74 379	69.16 382	75.49 390	80.72 411	54.07 432	74.94 447	80.30 415	58.34 438	70.01 372	81.19 408	52.50 309	86.54 389	53.37 413	71.09 418	85.87 405

USDC		70.33 385	68.37 386	76.21 381	80.60 413	56.23 407	79.19 401	86.49 320	60.89 414	61.29 457	85.47 330	31.78 469	89.47 348	53.37 413	76.21 364	82.94 446

LF4IMVS		64.02 433	62.19 436	69.50 444	70.90 482	53.29 440	76.13 433	77.18 444	52.65 466	58.59 467	80.98 412	23.55 485	76.52 461	53.06 415	66.66 438	78.68 468

PAPM		77.68 275	76.40 283	81.51 264	87.29 254	61.85 320	83.78 316	89.59 209	64.74 368	71.23 360	88.70 235	62.59 194	93.66 169	52.66 416	87.03 195	89.01 310

dmvs_re		71.14 373	70.58 367	72.80 422	81.96 393	59.68 358	75.60 440	79.34 426	68.55 312	69.27 385	80.72 416	49.42 361	76.54 460	52.56 417	77.79 337	82.19 452

CL-MVSNet_self_test		72.37 362	71.46 350	75.09 395	79.49 431	53.53 435	80.76 375	85.01 343	69.12 298	70.51 364	82.05 403	57.92 258	84.13 415	52.27 418	66.00 442	87.60 352

tpm cat1		70.57 381	68.31 387	77.35 372	82.41 388	57.95 377	78.08 418	80.22 417	52.04 467	68.54 393	77.66 446	52.00 320	87.84 377	51.77 419	72.07 412	86.25 393

our_test_3		69.14 399	67.00 411	75.57 387	79.80 426	58.80 365	77.96 420	77.81 436	59.55 427	62.90 452	78.25 442	47.43 376	83.97 416	51.71 420	67.58 436	83.93 434

MDTV_nov1_ep13			69.97 376		83.18 363	53.48 436	77.10 430	80.18 419	60.45 417	69.33 383	80.44 417	48.89 372	86.90 386	51.60 421	78.51 328

myMVS_eth3d28		73.62 337	73.53 327	73.90 411	88.20 195	47.41 472	78.06 419	79.37 425	74.29 164	73.98 324	84.29 356	44.67 405	83.54 421	51.47 422	87.39 187	90.74 240

JIA-IIPM		66.32 423	62.82 435	76.82 377	77.09 454	61.72 323	65.34 484	75.38 453	58.04 443	64.51 441	62.32 485	42.05 426	86.51 390	51.45 423	69.22 426	82.21 451

testing222		74.04 332	72.66 338	78.19 353	87.89 212	55.36 418	81.06 370	79.20 428	71.30 233	74.65 316	83.57 378	39.11 445	88.67 365	51.43 424	85.75 223	90.53 249

MSDG		73.36 345	70.99 360	80.49 293	84.51 331	65.80 210	80.71 377	86.13 328	65.70 351	65.46 433	83.74 371	44.60 406	90.91 316	51.13 425	76.89 348	84.74 424

PatchT		68.46 407	67.85 396	70.29 440	80.70 412	43.93 485	72.47 456	74.88 456	60.15 421	70.55 363	76.57 452	49.94 354	81.59 435	50.58 426	74.83 387	85.34 413

GG-mvs-BLEND				75.38 392	81.59 399	55.80 413	79.32 398	69.63 472		67.19 412	73.67 470	43.24 416	88.90 362	50.41 427	84.50 240	81.45 457

KD-MVS_2432*1600		66.22 424	63.89 427	73.21 416	75.47 463	53.42 437	70.76 464	84.35 349	64.10 377	66.52 423	78.52 439	34.55 463	84.98 408	50.40 428	50.33 484	81.23 458

miper_refine_blended		66.22 424	63.89 427	73.21 416	75.47 463	53.42 437	70.76 464	84.35 349	64.10 377	66.52 423	78.52 439	34.55 463	84.98 408	50.40 428	50.33 484	81.23 458

AllTest		70.96 375	68.09 391	79.58 325	85.15 314	63.62 273	84.58 294	79.83 420	62.31 403	60.32 462	86.73 290	32.02 467	88.96 360	50.28 430	71.57 415	86.15 396

TestCases				79.58 325	85.15 314	63.62 273		79.83 420	62.31 403	60.32 462	86.73 290	32.02 467	88.96 360	50.28 430	71.57 415	86.15 396

TAPA-MVS	73.13 9	79.15 233	77.94 238	82.79 232	89.59 132	62.99 298	88.16 168	91.51 143	65.77 350	77.14 252	91.09 160	60.91 229	93.21 201	50.26 432	87.05 194	92.17 192
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 428	62.91 433	71.38 431	75.85 459	56.60 400	69.12 472	74.66 460	57.28 450	54.12 479	77.87 444	45.85 396	74.48 477	49.95 433	61.52 464	83.05 443

MDA-MVSNet_test_wron		65.03 428	62.92 432	71.37 432	75.93 456	56.73 396	69.09 473	74.73 458	57.28 450	54.03 480	77.89 443	45.88 395	74.39 478	49.89 434	61.55 463	82.99 445

tpmvs		71.09 374	69.29 380	76.49 379	82.04 391	56.04 409	78.92 407	81.37 398	64.05 379	67.18 413	78.28 441	49.74 358	89.77 341	49.67 435	72.37 407	83.67 436

SD_0403		74.65 325	74.77 309	74.29 405	86.20 287	47.42 471	83.71 318	85.12 339	69.30 290	68.50 394	87.95 261	59.40 246	86.05 395	49.38 436	83.35 267	89.40 297

ppachtmachnet_test		70.04 389	67.34 408	78.14 354	79.80 426	61.13 331	79.19 401	80.59 406	59.16 431	65.27 435	79.29 432	46.75 385	87.29 383	49.33 437	66.72 437	86.00 402

UnsupCasMVSNet_bld		63.70 434	61.53 440	70.21 441	73.69 470	51.39 455	72.82 455	81.89 390	55.63 458	57.81 471	71.80 474	38.67 447	78.61 449	49.26 438	52.21 482	80.63 462

UWE-MVS		72.13 367	71.49 349	74.03 409	86.66 277	47.70 469	81.40 365	76.89 447	63.60 385	75.59 283	84.22 360	39.94 438	85.62 401	48.98 439	86.13 212	88.77 322

dp		66.80 418	65.43 419	70.90 439	79.74 428	48.82 468	75.12 445	74.77 457	59.61 426	64.08 445	77.23 449	42.89 418	80.72 442	48.86 440	66.58 439	83.16 441

FMVSNet5		69.50 396	67.96 393	74.15 407	82.97 375	55.35 419	80.01 390	82.12 389	62.56 400	63.02 449	81.53 407	36.92 455	81.92 434	48.42 441	74.06 393	85.17 418

thres100view900		76.50 295	75.55 294	79.33 330	89.52 135	56.99 393	85.83 260	83.23 368	73.94 172	76.32 270	87.12 284	51.89 325	91.95 262	48.33 442	83.75 256	89.07 303

tfpn200view9		76.42 301	75.37 299	79.55 327	89.13 158	57.65 384	85.17 275	83.60 360	73.41 189	76.45 266	86.39 308	52.12 315	91.95 262	48.33 442	83.75 256	89.07 303

thres400		76.50 295	75.37 299	79.86 312	89.13 158	57.65 384	85.17 275	83.60 360	73.41 189	76.45 266	86.39 308	52.12 315	91.95 262	48.33 442	83.75 256	90.00 276

LCM-MVSNet		54.25 447	49.68 457	67.97 455	53.73 503	45.28 480	66.85 479	80.78 403	35.96 492	39.45 493	62.23 486	8.70 502	78.06 453	48.24 445	51.20 483	80.57 463

RPMNet		73.51 339	70.49 369	82.58 241	81.32 407	65.19 229	75.92 436	92.27 95	57.60 446	72.73 341	76.45 453	52.30 312	95.43 78	48.14 446	77.71 338	87.11 376

thres600view7		76.50 295	75.44 295	79.68 322	89.40 143	57.16 390	85.53 269	83.23 368	73.79 176	76.26 271	87.09 285	51.89 325	91.89 265	48.05 447	83.72 259	90.00 276

TDRefinement		67.49 412	64.34 424	76.92 376	73.47 473	61.07 334	84.86 286	82.98 376	59.77 425	58.30 469	85.13 339	26.06 478	87.89 376	47.92 448	60.59 467	81.81 456

thres200		75.55 313	74.47 314	78.82 339	87.78 220	57.85 379	83.07 340	83.51 363	72.44 210	75.84 280	84.42 351	52.08 318	91.75 270	47.41 449	83.64 261	86.86 382

PVSNet_0	57.27 20	61.67 439	59.27 442	68.85 448	79.61 429	57.44 388	68.01 474	73.44 463	55.93 457	58.54 468	70.41 477	44.58 407	77.55 455	47.01 450	35.91 492	71.55 480

DP-MVS		76.78 291	74.57 311	83.42 196	93.29 52	69.46 105	88.55 150	83.70 359	63.98 381	70.20 368	88.89 231	54.01 298	94.80 113	46.66 451	81.88 287	86.01 400

COLMAP_ROB	66.92 17	73.01 353	70.41 371	80.81 286	87.13 258	65.63 214	88.30 163	84.19 354	62.96 392	63.80 448	87.69 266	38.04 451	92.56 235	46.66 451	74.91 386	84.24 429
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 380	69.30 379	74.88 398	84.52 330	56.35 406	75.87 438	79.42 424	64.59 369	67.76 402	82.41 396	41.10 431	81.54 436	46.64 453	81.34 291	86.75 386

LS3D		76.95 289	74.82 308	83.37 199	90.45 108	67.36 174	89.15 120	86.94 309	61.87 409	69.52 380	90.61 177	51.71 329	94.53 124	46.38 454	86.71 201	88.21 340

ETVMVS		72.25 365	71.05 359	75.84 383	87.77 222	51.91 448	79.39 397	74.98 455	69.26 292	73.71 327	82.95 388	40.82 434	86.14 394	46.17 455	84.43 245	89.47 295

MDA-MVSNet-bldmvs		66.68 419	63.66 429	75.75 384	79.28 434	60.56 348	73.92 453	78.35 434	64.43 371	50.13 485	79.87 427	44.02 412	83.67 418	46.10 456	56.86 471	83.03 444

new-patchmatchnet		61.73 438	61.73 438	61.70 464	72.74 479	24.50 507	69.16 471	78.03 435	61.40 411	56.72 474	75.53 465	38.42 448	76.48 462	45.95 457	57.67 470	84.13 431

WB-MVSnew		71.96 369	71.65 348	72.89 421	84.67 329	51.88 449	82.29 349	77.57 438	62.31 403	73.67 329	83.00 387	53.49 303	81.10 440	45.75 458	82.13 283	85.70 407

TinyColmap		67.30 415	64.81 422	74.76 400	81.92 395	56.68 399	80.29 385	81.49 396	60.33 418	56.27 477	83.22 382	24.77 482	87.66 380	45.52 459	69.47 424	79.95 465

pmmvs3		57.79 443	54.26 448	68.37 451	64.02 493	56.72 397	75.12 445	65.17 484	40.20 485	52.93 481	69.86 478	20.36 489	75.48 472	45.45 460	55.25 478	72.90 479

OpenMVS_ROB	64.09 19	70.56 382	68.19 388	77.65 366	80.26 416	59.41 363	85.01 282	82.96 377	58.76 436	65.43 434	82.33 398	37.63 453	91.23 298	45.34 461	76.03 365	82.32 450

test0.0.03 1		68.00 411	67.69 401	68.90 447	77.55 451	47.43 470	75.70 439	72.95 466	66.66 335	66.56 421	82.29 400	48.06 374	75.87 469	44.97 462	74.51 390	83.41 438

testgi		66.67 420	66.53 416	67.08 457	75.62 461	41.69 492	75.93 435	76.50 448	66.11 344	65.20 438	86.59 300	35.72 461	74.71 476	43.71 463	73.38 402	84.84 423

Anonymous20231206		68.60 403	67.80 399	71.02 437	80.23 418	50.75 460	78.30 417	80.47 409	56.79 452	66.11 429	82.63 395	46.35 390	78.95 448	43.62 464	75.70 368	83.36 439

FE-MVSNET		67.25 416	65.33 420	73.02 420	75.86 458	52.54 444	80.26 387	80.56 407	63.80 384	60.39 460	79.70 429	41.41 429	84.66 413	43.34 465	62.62 459	81.86 454

tfpnnormal		74.39 326	73.16 332	78.08 356	86.10 291	58.05 373	84.65 292	87.53 289	70.32 265	71.22 361	85.63 325	54.97 284	89.86 339	43.03 466	75.02 385	86.32 392

MIMVSNet1		68.58 404	66.78 415	73.98 410	80.07 421	51.82 450	80.77 374	84.37 348	64.40 373	59.75 465	82.16 402	36.47 458	83.63 419	42.73 467	70.33 421	86.48 391

usedtu_dtu_shiyan2		64.75 431	61.63 439	74.10 408	70.64 483	53.18 442	82.10 353	81.27 400	56.22 456	56.39 476	74.67 467	27.94 476	83.56 420	42.71 468	62.73 458	85.57 409

ttmdpeth		59.91 441	57.10 445	68.34 452	67.13 489	46.65 476	74.64 448	67.41 479	48.30 475	62.52 455	85.04 343	20.40 488	75.93 468	42.55 469	45.90 490	82.44 449

test20.03		67.45 413	66.95 412	68.94 446	75.48 462	44.84 483	77.50 425	77.67 437	66.66 335	63.01 450	83.80 369	47.02 380	78.40 450	42.53 470	68.86 429	83.58 437

ADS-MVSNet2		66.20 426	63.33 430	74.82 399	79.92 422	58.75 366	67.55 476	75.19 454	53.37 464	65.25 436	75.86 462	42.32 422	80.53 443	41.57 471	68.91 427	85.18 416

ADS-MVSNet		64.36 432	62.88 434	68.78 449	79.92 422	47.17 473	67.55 476	71.18 468	53.37 464	65.25 436	75.86 462	42.32 422	73.99 481	41.57 471	68.91 427	85.18 416

Patchmatch-test		64.82 430	63.24 431	69.57 443	79.42 432	49.82 464	63.49 490	69.05 475	51.98 469	59.95 464	80.13 423	50.91 339	70.98 485	40.66 473	73.57 398	87.90 346

MVS-HIRNet		59.14 442	57.67 444	63.57 462	81.65 397	43.50 486	71.73 458	65.06 485	39.59 487	51.43 482	57.73 491	38.34 449	82.58 429	39.53 474	73.95 394	64.62 486

WAC-MVS						42.58 488								39.46 475

myMVS_eth3d		67.02 417	66.29 417	69.21 445	84.68 326	42.58 488	78.62 410	73.08 464	66.65 338	66.74 419	79.46 430	31.53 470	82.30 431	39.43 476	76.38 361	82.75 447

DSMNet-mixed		57.77 444	56.90 446	60.38 466	67.70 487	35.61 497	69.18 470	53.97 498	32.30 496	57.49 472	79.88 426	40.39 436	68.57 491	38.78 477	72.37 407	76.97 472

N_pmnet		52.79 452	53.26 450	51.40 478	78.99 436	7.68 521	69.52 468	3.89 520	51.63 470	57.01 473	74.98 466	40.83 433	65.96 493	37.78 478	64.67 452	80.56 464

testing3		68.56 405	67.67 402	71.22 436	87.33 249	42.87 487	83.06 341	71.54 467	70.36 262	69.08 386	84.38 353	30.33 473	85.69 400	37.50 479	75.45 376	85.09 420

MVStest1		56.63 445	52.76 451	68.25 453	61.67 495	53.25 441	71.67 459	68.90 477	38.59 488	50.59 484	83.05 386	25.08 480	70.66 486	36.76 480	38.56 491	80.83 461

test_0402		72.79 359	70.44 370	79.84 313	88.13 200	65.99 203	85.93 255	84.29 351	65.57 353	67.40 411	85.49 329	46.92 381	92.61 231	35.88 481	74.38 391	80.94 460

new_pmnet		50.91 455	50.29 455	52.78 477	68.58 486	34.94 499	63.71 488	56.63 497	39.73 486	44.95 488	65.47 483	21.93 487	58.48 497	34.98 482	56.62 472	64.92 485

APD_test1		53.31 451	49.93 456	63.42 463	65.68 490	50.13 462	71.59 460	66.90 481	34.43 493	40.58 492	71.56 475	8.65 503	76.27 464	34.64 483	55.36 476	63.86 487

Syy-MVS		68.05 410	67.85 396	68.67 450	84.68 326	40.97 493	78.62 410	73.08 464	66.65 338	66.74 419	79.46 430	52.11 317	82.30 431	32.89 484	76.38 361	82.75 447

dmvs_testset		62.63 436	64.11 426	58.19 468	78.55 438	24.76 506	75.28 441	65.94 483	67.91 321	60.34 461	76.01 461	53.56 301	73.94 482	31.79 485	67.65 435	75.88 475

UWE-MVS-28		65.32 427	64.93 421	66.49 458	78.70 437	38.55 495	77.86 423	64.39 487	62.00 408	64.13 444	83.60 376	41.44 428	76.00 467	31.39 486	80.89 297	84.92 421

ANet_high		50.57 456	46.10 460	63.99 461	48.67 506	39.13 494	70.99 463	80.85 402	61.39 412	31.18 495	57.70 492	17.02 493	73.65 483	31.22 487	15.89 505	79.18 467

EGC-MVSNET		52.07 454	47.05 458	67.14 456	83.51 354	60.71 344	80.50 381	67.75 478	0.07 537	0.43 538	75.85 464	24.26 483	81.54 436	28.82 488	62.25 460	59.16 489

PMMVS2		40.82 463	38.86 467	46.69 479	53.84 501	16.45 514	48.61 496	49.92 499	37.49 489	31.67 494	60.97 487	8.14 504	56.42 499	28.42 489	30.72 496	67.19 484

tmp_tt		18.61 475	21.40 477	10.23 495	4.82 539	10.11 516	34.70 500	30.74 509	1.48 515	23.91 502	26.07 509	28.42 475	13.41 514	27.12 490	15.35 506	7.17 515

test_method		31.52 466	29.28 470	38.23 483	27.03 515	6.50 523	20.94 506	62.21 490	4.05 511	22.35 504	52.50 498	13.33 495	47.58 502	27.04 491	34.04 494	60.62 488

PDCNetPlus		24.75 472	22.46 476	31.64 489	35.53 510	17.00 513	32.00 503	9.46 513	18.43 503	18.56 508	51.31 499	1.65 510	33.00 509	26.51 492	8.70 511	44.91 500

RoMa-SfM		28.67 469	25.38 473	38.54 482	32.61 512	22.48 508	40.24 497	7.23 516	21.81 501	26.66 499	60.46 490	0.96 512	41.72 505	26.47 493	11.95 508	51.40 495

testf1		45.72 458	41.96 462	57.00 469	56.90 497	45.32 478	66.14 481	59.26 494	26.19 497	30.89 496	60.96 488	4.14 506	70.64 487	26.39 494	46.73 488	55.04 492

APD_test2		45.72 458	41.96 462	57.00 469	56.90 497	45.32 478	66.14 481	59.26 494	26.19 497	30.89 496	60.96 488	4.14 506	70.64 487	26.39 494	46.73 488	55.04 492

FPMVS		53.68 450	51.64 452	59.81 467	65.08 491	51.03 457	69.48 469	69.58 473	41.46 484	40.67 491	72.32 473	16.46 494	70.00 489	24.24 496	65.42 450	58.40 491

DKM		25.67 471	23.01 475	33.64 488	32.08 513	19.25 512	37.50 499	5.52 517	18.67 502	23.58 503	55.44 496	0.64 516	34.02 507	23.95 497	9.73 509	47.66 498

Gipuma		45.18 461	41.86 464	55.16 475	77.03 455	51.52 453	32.50 502	80.52 408	32.46 495	27.12 498	35.02 505	9.52 501	75.50 471	22.31 498	60.21 468	38.45 502
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		45.42 460	45.38 461	45.55 480	73.36 474	26.85 504	67.72 475	34.19 506	54.15 462	49.65 486	56.41 495	25.43 479	62.94 496	19.45 499	28.09 497	46.86 499

DeepMVS_CX				27.40 491	40.17 509	26.90 503		24.59 510	17.44 505	23.95 501	48.61 501	9.77 500	26.48 510	18.06 500	24.47 499	28.83 506

WB-MVS		54.94 446	54.72 447	55.60 474	73.50 471	20.90 509	74.27 452	61.19 491	59.16 431	50.61 483	74.15 468	47.19 379	75.78 470	17.31 501	35.07 493	70.12 481

PMVS	37.38 22	44.16 462	40.28 466	55.82 473	40.82 508	42.54 490	65.12 485	63.99 488	34.43 493	24.48 500	57.12 493	3.92 508	76.17 466	17.10 502	55.52 475	48.75 496
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 468	25.89 472	43.81 481	44.55 507	35.46 498	28.87 505	39.07 505	18.20 504	18.58 507	40.18 503	2.68 509	47.37 503	17.07 503	23.78 500	48.60 497
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		53.88 449	53.59 449	54.75 476	72.87 478	19.59 510	73.84 454	60.53 493	57.58 447	49.18 487	73.45 471	46.34 391	75.47 473	16.20 504	32.28 495	69.20 482

E-PMN		31.77 465	30.64 468	35.15 486	52.87 504	27.67 501	57.09 494	47.86 502	24.64 499	16.40 509	33.05 506	11.23 499	54.90 500	14.46 505	18.15 503	22.87 507

LoFTR		27.52 470	24.27 474	37.29 485	34.75 511	19.27 511	33.78 501	21.60 511	12.42 506	21.61 505	56.59 494	0.91 513	40.37 506	13.94 506	22.80 501	52.22 494

EMVS		30.81 467	29.65 469	34.27 487	50.96 505	25.95 505	56.58 495	46.80 503	24.01 500	15.53 510	30.68 508	12.47 496	54.43 501	12.81 507	17.05 504	22.43 508

kuosan		39.70 464	40.40 465	37.58 484	64.52 492	26.98 502	65.62 483	33.02 507	46.12 478	42.79 490	48.99 500	24.10 484	46.56 504	12.16 508	26.30 498	39.20 501

wuyk23d		16.82 476	15.94 479	19.46 494	58.74 496	31.45 500	39.22 498	3.74 522	6.84 508	6.04 513	2.70 537	1.27 511	24.29 512	10.54 509	14.40 507	2.63 520

ELoFTR		14.23 477	11.56 480	22.24 492	11.02 520	6.56 522	13.59 509	7.57 515	5.55 509	11.96 512	39.09 504	0.21 527	24.93 511	9.43 510	5.66 516	35.22 504

MatchFormer		22.13 473	19.86 478	28.93 490	28.66 514	15.74 515	31.91 504	17.10 512	7.75 507	18.87 506	47.50 502	0.62 518	33.92 508	7.49 511	18.87 502	37.14 503

GLUNet-SfM		12.90 478	10.00 481	21.62 493	13.58 519	8.30 519	10.19 511	9.30 514	4.31 510	12.18 511	30.90 507	0.50 522	22.76 513	4.89 512	4.14 522	33.79 505

SP-DiffGlue		4.29 487	4.46 490	3.77 503	3.68 540	2.12 530	5.97 516	2.22 524	1.10 516	4.89 516	13.93 514	0.66 515	1.95 524	2.47 513	5.24 517	7.22 514

XFeat-MNN		4.39 486	4.49 489	4.10 499	2.88 541	1.91 536	5.86 517	2.57 523	1.06 517	5.04 515	13.99 513	0.43 525	4.47 518	2.00 514	6.55 514	5.92 518

XFeat-NN		3.78 492	3.96 495	3.23 505	2.65 542	1.53 541	4.99 518	1.92 528	0.81 522	4.77 518	12.37 516	0.38 526	3.39 519	1.64 515	6.13 515	4.77 519

SP-LightGlue		4.27 488	4.41 491	3.86 500	10.99 521	1.99 533	8.19 512	2.06 526	0.98 519	2.37 521	8.29 517	0.56 520	2.10 521	1.27 516	4.99 518	7.48 511

SP-SuperGlue		4.24 489	4.38 492	3.81 502	10.75 522	2.00 532	8.18 513	2.09 525	1.00 518	2.41 520	8.29 517	0.56 520	2.05 523	1.27 516	4.91 519	7.39 512

SP-NN		4.00 491	4.12 494	3.63 504	9.92 524	1.81 538	7.94 515	1.90 529	0.86 520	2.15 523	8.00 520	0.50 522	2.09 522	1.20 518	4.63 521	6.98 516

SP-MNN		4.14 490	4.24 493	3.82 501	10.32 523	1.83 537	8.11 514	1.99 527	0.82 521	2.23 522	8.27 519	0.47 524	2.14 520	1.20 518	4.77 520	7.49 510

ALIKED-LG		8.61 479	8.70 483	8.33 496	20.63 516	8.70 518	15.50 507	4.61 518	2.19 512	5.84 514	18.70 510	0.80 514	8.06 515	1.03 520	8.97 510	8.25 509

ALIKED-MNN		7.86 480	7.83 486	7.97 497	19.40 517	8.86 517	14.48 508	3.90 519	1.59 513	4.74 519	16.49 511	0.59 519	7.65 516	0.91 521	8.34 513	7.39 512

ALIKED-NN		7.51 481	7.61 487	7.21 498	18.26 518	8.10 520	13.45 510	3.88 521	1.50 514	4.87 517	16.47 512	0.64 516	7.00 517	0.88 522	8.50 512	6.52 517

SIFT-NN		2.77 493	2.92 496	2.34 506	8.70 525	3.08 524	4.46 519	1.01 531	0.68 523	1.46 524	5.49 521	0.16 528	1.65 525	0.26 523	4.04 523	2.27 521

SIFT-MNN		2.63 494	2.75 497	2.25 507	8.10 526	2.84 525	4.08 520	1.02 530	0.68 523	1.28 525	5.34 524	0.15 529	1.64 526	0.26 523	3.88 525	2.27 521

testmvs		6.04 484	8.02 485	0.10 521	0.08 543	0.03 546	69.74 467	0.04 544	0.05 538	0.31 539	1.68 538	0.02 543	0.04 539	0.24 525	0.02 537	0.25 536

SIFT-NN-UMatch		2.26 498	2.39 501	1.89 512	6.21 534	2.08 531	3.76 522	0.83 534	0.66 525	1.04 529	5.09 525	0.14 530	1.52 529	0.23 526	3.51 527	2.07 525

SIFT-NN-NCMNet		2.52 495	2.64 498	2.14 508	7.53 528	2.74 526	4.00 521	0.98 532	0.65 526	1.24 527	5.08 527	0.14 530	1.60 527	0.23 526	3.94 524	2.07 525

SIFT-NN-CMatch		2.31 497	2.41 500	2.00 510	6.59 532	2.34 529	3.48 524	0.83 534	0.65 526	1.28 525	5.09 525	0.14 530	1.52 529	0.23 526	3.41 528	2.14 523

SIFT-UMatch		2.16 500	2.30 503	1.72 514	6.99 530	1.97 535	3.32 525	0.70 538	0.64 530	0.91 531	4.86 529	0.12 536	1.49 532	0.22 529	2.97 531	1.72 530

SIFT-ConvMatch		2.25 499	2.37 502	1.90 511	7.29 529	2.37 528	3.21 527	0.75 536	0.65 526	1.03 530	4.91 528	0.12 536	1.51 531	0.22 529	3.13 530	1.81 528

SIFT-NN-PointCN		2.07 501	2.18 504	1.74 513	5.75 535	1.65 540	3.27 526	0.73 537	0.60 533	1.07 528	4.62 531	0.13 533	1.43 533	0.21 531	3.22 529	2.12 524

test123		6.12 483	8.11 484	0.14 520	0.06 544	0.09 545	71.05 462	0.03 545	0.04 539	0.25 540	1.30 539	0.05 542	0.03 540	0.21 531	0.01 538	0.29 535

SIFT-UM-Cal		1.97 503	2.12 506	1.52 516	6.57 533	1.67 539	2.93 528	0.57 541	0.62 532	0.83 534	4.55 532	0.11 538	1.37 535	0.20 533	2.69 533	1.53 533

SIFT-NCM-Cal		2.40 496	2.52 499	2.05 509	7.74 527	2.54 527	3.75 523	0.84 533	0.65 526	0.89 532	4.78 530	0.13 533	1.60 527	0.19 534	3.71 526	2.01 527

SIFT-CM-Cal		2.02 502	2.13 505	1.67 515	6.79 531	1.99 533	2.79 529	0.64 539	0.63 531	0.87 533	4.48 533	0.13 533	1.41 534	0.19 534	2.70 532	1.61 532

SIFT-PCN-Cal		1.72 504	1.82 508	1.39 517	5.64 536	1.19 543	2.39 531	0.53 542	0.55 535	0.72 535	3.90 534	0.09 539	1.22 537	0.17 536	2.42 535	1.76 529

SIFT-PointCN		1.72 504	1.83 507	1.36 518	5.55 537	1.22 542	2.59 530	0.59 540	0.55 535	0.71 536	3.77 535	0.08 540	1.24 536	0.17 536	2.48 534	1.63 531

SIFT-NCMNet		1.44 506	1.56 509	1.08 519	5.14 538	1.07 544	1.97 532	0.32 543	0.56 534	0.64 537	3.23 536	0.07 541	1.01 538	0.14 538	1.95 536	1.15 534

mmdepth		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

monomultidepth		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

test_blank		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

uanet_test		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

DCPMVS		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

cdsmvs_eth3d_5k		19.96 474	26.61 471	0.00 522	0.00 545	0.00 547	0.00 533	89.26 227	0.00 540	0.00 541	88.61 239	61.62 213	0.00 541	0.00 539	0.00 539	0.00 537

pcd_1.5k_mvsjas		5.26 485	7.02 488	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	63.15 184	0.00 541	0.00 539	0.00 539	0.00 537

sosnet-low-res		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

sosnet		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

uncertanet		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

Regformer		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

ab-mvs-re		7.23 482	9.64 482	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	86.72 292	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

uanet		0.00 507	0.00 510	0.00 522	0.00 545	0.00 547	0.00 533	0.00 546	0.00 540	0.00 541	0.00 540	0.00 544	0.00 541	0.00 539	0.00 539	0.00 537

TestfortrainingZip				87.28 46	92.85 68	72.05 50	93.28 12	93.32 37	76.52 89	88.91 32	93.52 77	77.30 17	96.67 33		91.98 94	93.13 141

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 156	91.30 17

test_one_0601					95.07 7	71.46 60		94.14 9	78.27 41	92.05 13	95.74 8	80.83 12

eth-test2					0.00 545

eth-test					0.00 545

test_241102_ONE					95.30 2	70.98 73		94.06 14	77.17 67	93.10 1	95.39 18	82.99 1	97.27 14

save fliter					93.80 44	72.35 44	90.47 74	91.17 153	74.31 162

test0726					95.27 5	71.25 65	93.60 7	94.11 10	77.33 59	92.81 3	95.79 5	80.98 10

GSMVS																88.96 314

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 332				88.96 314

sam_mvs												50.01 352

MTGPA								92.02 113

test_post											5.46 522	50.36 348	84.24 414

patchmatchnet-post											74.00 469	51.12 338	88.60 366

MTMP							92.18 39	32.83 508

TEST9					93.26 56	72.96 25	88.75 138	91.89 121	68.44 315	85.00 81	93.10 89	74.36 33	95.41 81

test_8					93.13 60	72.57 35	88.68 144	91.84 125	68.69 310	84.87 85	93.10 89	74.43 31	95.16 91

agg_prior					92.85 68	71.94 53		91.78 129		84.41 96			94.93 102

test_prior4						72.60 34	89.01 125

test_prior				86.33 65	92.61 75	69.59 99		92.97 60					95.48 75			93.91 89

新几何2							86.29 246

旧先验1					91.96 81	65.79 211		86.37 323			93.08 93	69.31 101			92.74 80	88.74 325

原ACMM2							86.86 219

test222					91.50 87	68.26 138	84.16 310	83.20 371	54.63 461	79.74 190	91.63 138	58.97 249			91.42 104	86.77 385

segment_acmp												73.08 44

testdata1							84.14 311		75.71 116

test12				86.80 59	92.63 74	70.70 82		91.79 128		82.71 140		71.67 65	96.16 53		94.50 56	93.54 118

plane_prior7					90.08 117	68.51 132

plane_prior6					89.84 126	68.70 126						60.42 239

plane_prior4											91.00 165

plane_prior3						68.60 129			78.44 36	78.92 205

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 125

plane_prior						68.71 124	90.38 78		77.62 48						86.16 211

n2								0.00 546

nn								0.00 546

door-mid								69.98 471

test11								92.23 99

door								69.44 474

HQP5-MVS						66.98 185

HQP-NCC					89.33 146		89.17 116		76.41 95	77.23 246

ACMP_Plane					89.33 146		89.17 116		76.41 95	77.23 246

HQP4-MVS										77.24 245			95.11 95			91.03 227

HQP3-MVS								92.19 107							85.99 216

HQP2-MVS												60.17 242

NP-MVS					89.62 131	68.32 136					90.24 189

ACMMP++_ref															81.95 286

ACMMP++															81.25 292

Test By Simon												64.33 170

High-resolution multi-view benchmark