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 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 144	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 229	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 139	86.70 266	65.83 205	88.77 136	89.78 190	75.46 115	88.35 36	93.73 74	69.19 99	93.06 205	91.30 3	88.44 159	94.02 76

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 202	87.08 255	65.21 221	89.09 123	90.21 178	79.67 19	89.98 24	95.02 24	73.17 42	91.71 264	91.30 3	91.60 99	92.34 168

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 97	79.94 17	89.74 27	94.86 26	68.63 109	94.20 136	90.83 5	91.39 104	94.38 56

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 201	82.14 3	86.65 66	94.28 46	68.28 115	97.46 6	90.81 6	95.31 38	95.15 8

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 241	67.30 174	89.50 101	90.98 149	76.25 96	90.56 22	94.75 29	68.38 112	94.24 135	90.80 7	92.32 89	94.19 66

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 311	69.51 100	89.62 98	90.58 162	73.42 179	87.75 50	94.02 61	72.85 48	93.24 189	90.37 8	90.75 115	93.96 78

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 363	69.39 107	89.65 95	90.29 176	73.31 183	87.77 49	94.15 55	71.72 61	93.23 190	90.31 9	90.67 117	93.89 84

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 405	69.03 110	89.47 102	89.65 197	73.24 187	86.98 62	94.27 47	66.62 133	93.23 190	90.26 10	89.95 130	93.78 93

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 185	87.12 254	66.01 199	88.56 148	89.43 205	75.59 111	89.32 28	94.32 44	72.89 46	91.21 289	90.11 11	92.33 87	93.16 128

fmvsm_s_conf0.5_n_2		84.04 94	84.11 95	83.81 176	86.17 279	65.00 229	86.96 206	87.28 282	74.35 151	88.25 39	94.23 50	61.82 199	92.60 223	89.85 12	88.09 164	93.84 87

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 146	86.26 275	67.40 170	89.18 115	89.31 214	72.50 198	88.31 37	93.86 70	69.66 92	91.96 252	89.81 13	91.05 109	93.38 114

fmvsm_s_conf0.1_n_2		83.80 101	83.79 101	83.83 174	85.62 292	64.94 233	87.03 203	86.62 300	74.32 152	87.97 47	94.33 43	60.67 223	92.60 223	89.72 14	87.79 170	93.96 78

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 53

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 53

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 33
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	83.60 180	87.32 243	65.13 224	88.86 130	91.63 128	75.41 116	88.23 40	93.45 81	68.56 110	92.47 231	89.52 18	92.78 79	93.20 126

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 33

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 109	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 52
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	84.00 167	87.78 218	66.09 196	89.96 86	90.80 157	77.37 57	86.72 65	94.20 52	72.51 51	92.78 219	89.08 22	92.33 87	93.13 132

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 118
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 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 65

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 64

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 246	68.54 130	89.57 99	90.44 167	75.31 120	87.49 54	94.39 42	72.86 47	92.72 220	89.04 27	90.56 118	94.16 67

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 319	92.39 6				88.94 28	96.63 4	94.85 21

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 143	85.42 298	68.81 116	88.49 150	87.26 284	68.08 308	88.03 44	93.49 77	72.04 57	91.77 260	88.90 29	89.14 146	92.24 175

fmvsm_s_conf0.5_n		83.80 101	83.71 103	84.07 156	86.69 267	67.31 173	89.46 103	83.07 354	71.09 229	86.96 63	93.70 75	69.02 105	91.47 279	88.79 30	84.62 228	93.44 113

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 129	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_fmvsmvis_n_1920		84.02 95	83.87 97	84.49 127	84.12 329	69.37 108	88.15 166	87.96 265	70.01 262	83.95 107	93.23 86	68.80 107	91.51 277	88.61 32	89.96 129	92.57 156

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 142	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	96.91 1	94.87 18
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		83.56 112	83.38 111	84.10 150	84.86 313	67.28 175	89.40 108	83.01 355	70.67 241	87.08 60	93.96 67	68.38 112	91.45 280	88.56 34	84.50 229	93.56 108

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 281	69.93 92	88.65 144	90.78 158	69.97 264	88.27 38	93.98 66	71.39 67	91.54 274	88.49 35	90.45 120	93.91 81

CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 59

fmvsm_l_conf0.5_n_a		84.13 93	84.16 94	84.06 159	85.38 299	68.40 133	88.34 158	86.85 294	67.48 315	87.48 55	93.40 82	70.89 73	91.61 265	88.38 37	89.22 143	92.16 182

fmvsm_s_conf0.5_n_a		83.63 110	83.41 110	84.28 141	86.14 280	68.12 143	89.43 104	82.87 359	70.27 257	87.27 59	93.80 73	69.09 100	91.58 267	88.21 38	83.65 249	93.14 131

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 121	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 29

MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 93	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 29

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 93	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 29

fmvsm_s_conf0.1_n_a		83.32 121	82.99 118	84.28 141	83.79 337	68.07 145	89.34 111	82.85 360	69.80 268	87.36 58	94.06 59	68.34 114	91.56 270	87.95 42	83.46 255	93.21 124

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 146	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	95.04 10
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 95	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 72

SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 158	87.63 45	94.27 65	93.65 101
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 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 137

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 102	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 80
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 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 107	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 96

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 164	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 224	92.02 106	79.45 22	85.88 70	94.80 27	68.07 117	96.21 50	86.69 52	95.34 36	93.23 121

fmvsm_s_conf0.5_n_7		83.34 119	84.03 96	81.28 261	85.73 289	65.13 224	85.40 265	89.90 188	74.96 135	82.13 139	93.89 69	66.65 132	87.92 353	86.56 53	91.05 109	90.80 224

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 133	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 135

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 133	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 135

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 143	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 128

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 121	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 49

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 42

MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 104
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 130	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

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 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 61

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 99	83.81 110	93.95 68	69.77 91	96.01 58	85.15 62	94.66 51	94.32 61
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 49	93.26 56	72.96 25	88.75 138	91.89 114	68.69 298	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 139

test9_res														84.90 64	95.70 30	92.87 146

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 71

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 191	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 55

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 130	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 101
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 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 147	86.84 64	94.65 31	67.31 126	95.77 64	84.80 68	92.85 78	92.84 149

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 196	84.64 90	91.71 125	71.85 58	96.03 55	84.77 69	94.45 60	94.49 51

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 234	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

PC_three_1452									68.21 307	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 97

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 106	96.65 34	84.53 72	94.90 45	94.00 77

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 100	96.70 31	84.37 74	94.83 49	94.03 75

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 153	91.43 140	70.34 79	97.23 17	84.26 75	93.36 74	94.37 57

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 193	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 151
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 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 119	95.95 62	84.20 78	94.39 61	93.23 121

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 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 83

BP-MVS1		84.32 91	83.71 103	86.17 68	87.84 213	67.85 154	89.38 109	89.64 198	77.73 45	83.98 106	92.12 114	56.89 261	95.43 77	84.03 80	91.75 98	95.24 7

EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 118	69.04 104	95.43 77	83.93 81	93.77 69	93.01 140

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 16

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 229	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 124	83.49 83	91.14 108	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	84.06 159	91.71 84	64.94 233	86.47 227	91.87 116	73.63 171	86.60 67	93.02 93	76.57 18	91.87 258	83.36 84	92.15 90	95.35 3

test_prior2							88.85 132		75.41 116	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 199	85.22 78	91.90 117	69.47 94	96.42 44	83.28 86	95.94 23	94.35 58

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 124	96.60 37	83.06 87	94.50 57	94.07 73

X-MVStestdata		80.37 194	77.83 234	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 479	67.45 124	96.60 37	83.06 87	94.50 57	94.07 73

mamv4		76.81 281	78.23 225	72.54 402	86.12 281	65.75 210	78.76 388	82.07 368	64.12 358	72.97 326	91.02 157	67.97 118	68.08 467	83.04 89	78.02 322	83.80 412

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 175	85.94 69	94.51 35	65.80 149	95.61 67	83.04 89	92.51 83	93.53 111

agg_prior2														82.91 91	95.45 33	92.70 151

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 95	76.87 74	82.81 131	94.25 49	66.44 137	96.24 49	82.88 92	94.28 64	93.38 114

diffmvs_AUTHOR		82.38 138	82.27 134	82.73 228	83.26 351	63.80 262	83.89 306	89.76 192	73.35 182	82.37 134	90.84 161	66.25 140	90.79 301	82.77 93	87.93 168	93.59 106

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 89	73.53 176	85.69 73	94.45 37	65.00 157	95.56 68	82.75 94	91.87 95	92.50 161

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 89	73.53 176	85.69 73	94.45 37	63.87 165		82.75 94	91.87 95	92.50 161

h-mvs33		83.15 124	82.19 135	86.02 76	90.56 105	70.85 79	88.15 166	89.16 224	76.02 100	84.67 87	91.39 141	61.54 204	95.50 73	82.71 96	75.48 360	91.72 195

hse-mvs2		81.72 150	80.94 155	84.07 156	88.72 175	67.68 160	85.87 249	87.26 284	76.02 100	84.67 87	88.22 242	61.54 204	93.48 177	82.71 96	73.44 388	91.06 214

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 112	83.86 108	94.42 40	67.87 121	96.64 35	82.70 98	94.57 56	93.66 97

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 167	93.82 72	64.33 161	96.29 46	82.67 99	90.69 116	93.23 121
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		82.10 141	81.88 143	82.76 226	83.00 361	63.78 264	83.68 311	89.76 192	72.94 194	82.02 141	89.85 186	65.96 148	90.79 301	82.38 100	87.30 180	93.71 95
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		83.65 108	84.54 89	80.99 270	90.06 120	65.83 205	84.21 299	88.74 247	71.60 217	85.01 79	92.44 105	74.51 29	83.50 399	82.15 101	92.15 90	93.64 103

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 116	91.20 148	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 282	76.41 86	85.80 71	90.22 181	74.15 35	95.37 85	81.82 103	91.88 94	92.65 155

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 97	87.73 52	91.46 139	70.32 80	93.78 158	81.51 104	88.95 147	94.63 39

sasdasda		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 144	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 144	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

baseline		84.93 86	84.98 83	84.80 117	87.30 244	65.39 218	87.30 196	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

MGCFI-Net		85.06 85	85.51 74	83.70 178	89.42 139	63.01 286	89.43 104	92.62 78	76.43 85	87.53 53	91.34 142	72.82 49	93.42 182	81.28 108	88.74 153	94.66 36

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 246	65.77 209	87.75 180	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 150	81.27 109	90.48 119	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 87	86.32 65	91.65 85	72.70 30	85.98 245	90.33 173	76.11 98	82.08 140	91.61 133	71.36 68	94.17 139	81.02 110	92.58 82	92.08 184

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 129	73.89 165	82.67 133	94.09 57	62.60 183	95.54 70	80.93 111	92.93 77	93.57 107

CPTT-MVS		83.73 105	83.33 113	84.92 111	93.28 53	70.86 78	92.09 41	90.38 169	68.75 297	79.57 182	92.83 97	60.60 227	93.04 208	80.92 112	91.56 102	90.86 223

ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 142	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 310	69.32 97	95.38 82	80.82 113	91.37 105	92.72 150

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 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 67
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 99	83.53 108	84.96 107	86.77 264	69.28 109	90.46 75	92.67 72	74.79 141	82.95 125	91.33 143	72.70 50	93.09 203	80.79 115	79.28 308	92.50 161

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 100	79.31 24	84.39 96	92.18 109	64.64 159	95.53 71	80.70 116	94.65 52	94.56 46

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 262	79.31 24	84.39 96	92.18 109	64.64 159	95.53 71	80.70 116	90.91 113	93.21 124

EI-MVSNet-Vis-set		84.19 92	83.81 100	85.31 93	88.18 194	67.85 154	87.66 182	89.73 195	80.05 15	82.95 125	89.59 199	70.74 76	94.82 108	80.66 118	84.72 226	93.28 120

MSLP-MVS++		85.43 75	85.76 69	84.45 128	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 128	92.94 210	80.36 119	94.35 63	90.16 253

MVS_111021_LR		82.61 135	82.11 136	84.11 149	88.82 166	71.58 57	85.15 270	86.16 308	74.69 143	80.47 172	91.04 154	62.29 190	90.55 307	80.33 120	90.08 127	90.20 252

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 264	93.44 32	78.70 34	83.63 115	89.03 214	74.57 27	95.71 66	80.26 121	94.04 67	93.66 97
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		83.52 113	82.64 125	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 197	83.71 111	91.86 120	55.69 268	95.35 86	80.03 122	89.74 134	94.69 32

EI-MVSNet-UG-set		83.81 100	83.38 111	85.09 103	87.87 211	67.53 166	87.44 191	89.66 196	79.74 18	82.23 137	89.41 208	70.24 82	94.74 114	79.95 123	83.92 241	92.99 142

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 162	83.16 122	91.07 153	75.94 21	95.19 89	79.94 124	94.38 62	93.55 109

RRT-MVS		82.60 137	82.10 137	84.10 150	87.98 207	62.94 291	87.45 190	91.27 140	77.42 56	79.85 178	90.28 177	56.62 264	94.70 117	79.87 125	88.15 163	94.67 33

AstraMVS		80.81 173	80.14 174	82.80 220	86.05 284	63.96 257	86.46 228	85.90 312	73.71 169	80.85 165	90.56 170	54.06 285	91.57 269	79.72 126	83.97 240	92.86 147

OPM-MVS		83.50 114	82.95 119	85.14 98	88.79 172	70.95 74	89.13 121	91.52 133	77.55 52	80.96 161	91.75 124	60.71 221	94.50 124	79.67 127	86.51 195	89.97 269
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
E2		84.00 96	83.87 97	84.39 130	87.70 226	64.95 230	86.40 232	92.23 92	75.85 103	83.21 118	91.78 122	70.09 85	93.55 170	79.52 128	88.05 165	94.66 36

E3		84.00 96	83.87 97	84.39 130	87.70 226	64.95 230	86.40 232	92.23 92	75.85 103	83.21 118	91.78 122	70.09 85	93.55 170	79.52 128	88.05 165	94.66 36

viewcassd2359sk11		83.89 98	83.74 102	84.34 135	87.76 221	64.91 236	86.30 236	92.22 95	75.47 114	83.04 124	91.52 135	70.15 83	93.53 173	79.26 130	87.96 167	94.57 44

E3new		83.78 103	83.60 106	84.31 137	87.76 221	64.89 237	86.24 239	92.20 98	75.15 130	82.87 127	91.23 144	70.11 84	93.52 175	79.05 131	87.79 170	94.51 50

viewmacassd2359aftdt		83.76 104	83.66 105	84.07 156	86.59 270	64.56 242	86.88 211	91.82 119	75.72 106	83.34 117	92.15 113	68.24 116	92.88 213	79.05 131	89.15 145	94.77 25

viewmanbaseed2359cas		83.66 107	83.55 107	84.00 167	86.81 262	64.53 243	86.65 221	91.75 124	74.89 137	83.15 123	91.68 126	68.74 108	92.83 217	79.02 133	89.24 142	94.63 39

LuminaMVS		80.68 181	79.62 190	83.83 174	85.07 310	68.01 148	86.99 205	88.83 239	70.36 252	81.38 152	87.99 250	50.11 333	92.51 230	79.02 133	86.89 189	90.97 219

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 306	84.61 91	93.48 78	72.32 52	96.15 53	79.00 135	95.43 34	94.28 63

MVSFormer		82.85 131	82.05 139	85.24 95	87.35 236	70.21 86	90.50 72	90.38 169	68.55 301	81.32 153	89.47 202	61.68 201	93.46 179	78.98 136	90.26 123	92.05 185

test_djsdf		80.30 197	79.32 198	83.27 193	83.98 333	65.37 219	90.50 72	90.38 169	68.55 301	76.19 262	88.70 225	56.44 265	93.46 179	78.98 136	80.14 298	90.97 219

test_vis1_n_1920		75.52 303	75.78 279	74.75 379	79.84 411	57.44 367	83.26 323	85.52 316	62.83 376	79.34 189	86.17 303	45.10 384	79.71 421	78.75 138	81.21 282	87.10 356

HQP_MVS		83.64 109	83.14 114	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 194	91.00 158	60.42 229	95.38 82	78.71 139	86.32 197	91.33 206

plane_prior5								92.44 82					95.38 82	78.71 139	86.32 197	91.33 206

LPG-MVS_test		82.08 142	81.27 148	84.50 125	89.23 152	68.76 119	90.22 81	91.94 112	75.37 118	76.64 250	91.51 136	54.29 281	94.91 102	78.44 141	83.78 242	89.83 274

LGP-MVS_train				84.50 125	89.23 152	68.76 119		91.94 112	75.37 118	76.64 250	91.51 136	54.29 281	94.91 102	78.44 141	83.78 242	89.83 274

lupinMVS		81.39 162	80.27 170	84.76 119	87.35 236	70.21 86	85.55 260	86.41 302	62.85 375	81.32 153	88.61 229	61.68 201	92.24 243	78.41 143	90.26 123	91.83 188

jason		81.39 162	80.29 169	84.70 121	86.63 269	69.90 94	85.95 246	86.77 295	63.24 368	81.07 159	89.47 202	61.08 217	92.15 245	78.33 144	90.07 128	92.05 185
jason: jason.
xiu_mvs_v1_base_debu		80.80 176	79.72 187	84.03 164	87.35 236	70.19 88	85.56 257	88.77 243	69.06 290	81.83 142	88.16 243	50.91 322	92.85 214	78.29 145	87.56 174	89.06 294

xiu_mvs_v1_base		80.80 176	79.72 187	84.03 164	87.35 236	70.19 88	85.56 257	88.77 243	69.06 290	81.83 142	88.16 243	50.91 322	92.85 214	78.29 145	87.56 174	89.06 294

xiu_mvs_v1_base_debi		80.80 176	79.72 187	84.03 164	87.35 236	70.19 88	85.56 257	88.77 243	69.06 290	81.83 142	88.16 243	50.91 322	92.85 214	78.29 145	87.56 174	89.06 294

guyue		81.13 166	80.64 160	82.60 231	86.52 271	63.92 260	86.69 220	87.73 273	73.97 161	80.83 166	89.69 193	56.70 262	91.33 285	78.26 148	85.40 219	92.54 158

Effi-MVS+		83.62 111	83.08 115	85.24 95	88.38 188	67.45 167	88.89 129	89.15 225	75.50 113	82.27 136	88.28 239	69.61 93	94.45 127	77.81 149	87.84 169	93.84 87

KinetiMVS		83.31 122	82.61 126	85.39 91	87.08 255	67.56 165	88.06 168	91.65 127	77.80 44	82.21 138	91.79 121	57.27 256	94.07 142	77.77 150	89.89 132	94.56 46

viewdifsd2359ckpt07		82.83 132	82.78 124	82.99 209	86.51 272	62.58 294	85.09 273	90.83 156	75.22 123	82.28 135	91.63 130	69.43 95	92.03 248	77.71 151	86.32 197	94.34 59

PS-MVSNAJss		82.07 143	81.31 147	84.34 135	86.51 272	67.27 176	89.27 112	91.51 134	71.75 212	79.37 187	90.22 181	63.15 175	94.27 131	77.69 152	82.36 270	91.49 202

ACMP	74.13 6	81.51 161	80.57 161	84.36 133	89.42 139	68.69 126	89.97 85	91.50 137	74.46 149	75.04 297	90.41 173	53.82 287	94.54 121	77.56 153	82.91 262	89.86 273
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
BP-MVS														77.47 154

HQP-MVS		82.61 135	82.02 140	84.37 132	89.33 144	66.98 183	89.17 116	92.19 100	76.41 86	77.23 235	90.23 180	60.17 232	95.11 94	77.47 154	85.99 206	91.03 216

MVS_Test		83.15 124	83.06 116	83.41 189	86.86 259	63.21 282	86.11 243	92.00 108	74.31 153	82.87 127	89.44 207	70.03 87	93.21 192	77.39 156	88.50 158	93.81 89

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 249	93.37 83	60.40 231	96.75 30	77.20 157	93.73 70	95.29 6

anonymousdsp		78.60 238	77.15 254	82.98 211	80.51 403	67.08 181	87.24 198	89.53 202	65.66 339	75.16 292	87.19 272	52.52 296	92.25 242	77.17 158	79.34 307	89.61 281

mmtdpeth		74.16 319	73.01 323	77.60 347	83.72 340	61.13 315	85.10 272	85.10 321	72.06 208	77.21 239	80.33 408	43.84 393	85.75 376	77.14 159	52.61 457	85.91 381

VDD-MVS		83.01 129	82.36 131	84.96 107	91.02 95	66.40 191	88.91 128	88.11 258	77.57 49	84.39 96	93.29 85	52.19 302	93.91 152	77.05 160	88.70 154	94.57 44

XVG-OURS-SEG-HR		80.81 173	79.76 184	83.96 171	85.60 293	68.78 118	83.54 318	90.50 165	70.66 244	76.71 248	91.66 127	60.69 222	91.26 286	76.94 161	81.58 278	91.83 188

Elysia		81.53 157	80.16 172	85.62 84	85.51 295	68.25 139	88.84 133	92.19 100	71.31 222	80.50 170	89.83 187	46.89 362	94.82 108	76.85 162	89.57 136	93.80 91

StellarMVS		81.53 157	80.16 172	85.62 84	85.51 295	68.25 139	88.84 133	92.19 100	71.31 222	80.50 170	89.83 187	46.89 362	94.82 108	76.85 162	89.57 136	93.80 91

jajsoiax		79.29 220	77.96 228	83.27 193	84.68 318	66.57 190	89.25 113	90.16 180	69.20 286	75.46 277	89.49 201	45.75 379	93.13 201	76.84 164	80.80 288	90.11 257

SDMVSNet		80.38 192	80.18 171	80.99 270	89.03 161	64.94 233	80.45 363	89.40 206	75.19 127	76.61 252	89.98 183	60.61 226	87.69 357	76.83 165	83.55 251	90.33 247

viewdifsd2359ckpt11		80.37 194	79.73 185	82.30 237	83.70 341	62.39 298	84.20 300	86.67 296	73.22 188	80.90 162	90.62 167	63.00 180	91.56 270	76.81 166	78.44 315	92.95 144

viewmsd2359difaftdt		80.37 194	79.73 185	82.30 237	83.70 341	62.39 298	84.20 300	86.67 296	73.22 188	80.90 162	90.62 167	63.00 180	91.56 270	76.81 166	78.44 315	92.95 144

mvs_tets		79.13 224	77.77 238	83.22 197	84.70 317	66.37 192	89.17 116	90.19 179	69.38 278	75.40 280	89.46 204	44.17 391	93.15 199	76.78 168	80.70 290	90.14 254

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 200	93.04 46	69.80 268	82.85 129	91.22 147	73.06 44	96.02 57	76.72 169	94.63 54	91.46 205

test_cas_vis1_n_1920		73.76 325	73.74 314	73.81 389	75.90 435	59.77 335	80.51 361	82.40 364	58.30 417	81.62 150	85.69 311	44.35 390	76.41 439	76.29 170	78.61 311	85.23 391

ET-MVSNet_ETH3D		78.63 237	76.63 269	84.64 122	86.73 265	69.47 102	85.01 275	84.61 327	69.54 275	66.51 404	86.59 290	50.16 332	91.75 261	76.26 171	84.24 237	92.69 153

viewdifsd2359ckpt09		83.34 119	82.55 127	85.70 81	87.64 230	67.72 159	88.43 151	91.68 126	71.91 211	81.65 149	90.68 165	67.10 129	94.75 113	76.17 172	87.70 173	94.62 41

v2v482		80.23 198	79.29 199	83.05 206	83.62 343	64.14 254	87.04 202	89.97 185	73.61 172	78.18 213	87.22 270	61.10 216	93.82 156	76.11 173	76.78 339	91.18 210

test_fmvs1_n		70.86 360	70.24 356	72.73 400	72.51 458	55.28 399	81.27 349	79.71 399	51.49 447	78.73 196	84.87 333	27.54 453	77.02 433	76.06 174	79.97 300	85.88 382

CLD-MVS		82.31 139	81.65 145	84.29 140	88.47 183	67.73 158	85.81 253	92.35 87	75.78 105	78.33 209	86.58 292	64.01 164	94.35 128	76.05 175	87.48 177	90.79 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPNet		83.72 106	82.92 120	86.14 72	84.22 327	69.48 101	91.05 64	85.27 318	81.30 6	76.83 244	91.65 128	66.09 144	95.56 68	76.00 176	93.85 68	93.38 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewdifsd2359ckpt13		82.91 130	82.29 133	84.77 118	86.96 258	66.90 187	87.47 187	91.62 129	72.19 204	81.68 148	90.71 164	66.92 130	93.28 185	75.90 177	87.15 183	94.12 70

test_fmvs1		70.93 359	70.52 351	72.16 404	73.71 447	55.05 401	80.82 352	78.77 408	51.21 448	78.58 201	84.41 341	31.20 448	76.94 434	75.88 178	80.12 299	84.47 403

XVG-OURS		80.41 190	79.23 201	83.97 170	85.64 291	69.02 112	83.03 331	90.39 168	71.09 229	77.63 226	91.49 138	54.62 280	91.35 283	75.71 179	83.47 254	91.54 199

V42		79.38 218	78.24 223	82.83 217	81.10 397	65.50 215	85.55 260	89.82 189	71.57 218	78.21 211	86.12 304	60.66 224	93.18 198	75.64 180	75.46 362	89.81 276

PS-MVSNAJ		81.69 152	81.02 153	83.70 178	89.51 134	68.21 142	84.28 298	90.09 182	70.79 238	81.26 157	85.62 315	63.15 175	94.29 129	75.62 181	88.87 149	88.59 318

xiu_mvs_v2_base		81.69 152	81.05 152	83.60 180	89.15 155	68.03 147	84.46 291	90.02 183	70.67 241	81.30 156	86.53 295	63.17 174	94.19 138	75.60 182	88.54 156	88.57 319

EIA-MVS		83.31 122	82.80 122	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 145	78.96 192	86.42 297	69.06 102	95.26 87	75.54 183	90.09 126	93.62 104

AUN-MVS		79.21 222	77.60 244	84.05 162	88.71 176	67.61 162	85.84 251	87.26 284	69.08 289	77.23 235	88.14 247	53.20 294	93.47 178	75.50 184	73.45 387	91.06 214

mvsmamba		80.60 185	79.38 195	84.27 143	89.74 128	67.24 178	87.47 187	86.95 290	70.02 261	75.38 281	88.93 219	51.24 319	92.56 226	75.47 185	89.22 143	93.00 141

reproduce_monomvs		75.40 307	74.38 305	78.46 327	83.92 335	57.80 360	83.78 308	86.94 291	73.47 178	72.25 337	84.47 339	38.74 424	89.27 328	75.32 186	70.53 407	88.31 324

OMC-MVS		82.69 133	81.97 142	84.85 114	88.75 174	67.42 168	87.98 170	90.87 154	74.92 136	79.72 180	91.65 128	62.19 193	93.96 144	75.26 187	86.42 196	93.16 128

VortexMVS		78.57 240	77.89 232	80.59 279	85.89 285	62.76 293	85.61 254	89.62 199	72.06 208	74.99 298	85.38 321	55.94 267	90.77 304	74.99 188	76.58 340	88.23 325

v1144		80.03 202	79.03 205	83.01 208	83.78 338	64.51 245	87.11 201	90.57 164	71.96 210	78.08 216	86.20 302	61.41 208	93.94 147	74.93 189	77.23 330	90.60 235

MVSTER		79.01 227	77.88 233	82.38 235	83.07 358	64.80 239	84.08 305	88.95 236	69.01 293	78.69 197	87.17 273	54.70 278	92.43 233	74.69 190	80.57 292	89.89 272

viewmambaseed2359dif		80.41 190	79.84 182	82.12 239	82.95 365	62.50 297	83.39 319	88.06 262	67.11 317	80.98 160	90.31 176	66.20 142	91.01 297	74.62 191	84.90 223	92.86 147

test_vis1_n		69.85 374	69.21 363	71.77 406	72.66 457	55.27 400	81.48 345	76.21 427	52.03 444	75.30 288	83.20 371	28.97 451	76.22 441	74.60 192	78.41 319	83.81 411

test_fmvs2		68.35 387	67.48 386	70.98 415	69.50 461	51.95 424	80.05 370	76.38 426	49.33 450	74.65 305	84.38 342	23.30 462	75.40 450	74.51 193	75.17 371	85.60 385

PVSNet_Blended_VisFu		82.62 134	81.83 144	84.96 107	90.80 101	69.76 97	88.74 140	91.70 125	69.39 277	78.96 192	88.46 234	65.47 151	94.87 107	74.42 194	88.57 155	90.24 251

v8		79.97 204	79.02 206	82.80 220	84.09 330	64.50 247	87.96 171	90.29 176	74.13 160	75.24 290	86.81 279	62.88 182	93.89 155	74.39 195	75.40 365	90.00 265

v144192		79.47 212	78.37 219	82.78 224	83.35 348	63.96 257	86.96 206	90.36 172	69.99 263	77.50 227	85.67 313	60.66 224	93.77 160	74.27 196	76.58 340	90.62 233

ACMM	73.20 8	80.78 180	79.84 182	83.58 182	89.31 147	68.37 134	89.99 84	91.60 131	70.28 256	77.25 233	89.66 195	53.37 292	93.53 173	74.24 197	82.85 263	88.85 307
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
旧先验2							86.56 225		58.10 420	87.04 61			88.98 335	74.07 198

v1192		79.59 209	78.43 218	83.07 205	83.55 345	64.52 244	86.93 209	90.58 162	70.83 237	77.78 223	85.90 306	59.15 238	93.94 147	73.96 199	77.19 332	90.76 227

v10		79.74 206	78.67 211	82.97 212	84.06 331	64.95 230	87.88 177	90.62 161	73.11 190	75.11 294	86.56 293	61.46 207	94.05 143	73.68 200	75.55 358	89.90 271

v1921920		79.22 221	78.03 227	82.80 220	83.30 350	63.94 259	86.80 214	90.33 173	69.91 266	77.48 228	85.53 317	58.44 244	93.75 162	73.60 201	76.85 337	90.71 231

cl22		78.07 252	77.01 256	81.23 263	82.37 378	61.83 309	83.55 316	87.98 264	68.96 294	75.06 296	83.87 353	61.40 209	91.88 257	73.53 202	76.39 345	89.98 268

Effi-MVS+-dtu		80.03 202	78.57 214	84.42 129	85.13 308	68.74 121	88.77 136	88.10 259	74.99 132	74.97 299	83.49 366	57.27 256	93.36 183	73.53 202	80.88 286	91.18 210

c3_l		78.75 233	77.91 230	81.26 262	82.89 366	61.56 312	84.09 304	89.13 227	69.97 264	75.56 273	84.29 345	66.36 138	92.09 247	73.47 204	75.48 360	90.12 256

VDDNet		81.52 159	80.67 159	84.05 162	90.44 108	64.13 255	89.73 93	85.91 311	71.11 228	83.18 121	93.48 78	50.54 328	93.49 176	73.40 205	88.25 161	94.54 48

CANet_DTU		80.61 183	79.87 181	82.83 217	85.60 293	63.17 285	87.36 193	88.65 251	76.37 90	75.88 268	88.44 235	53.51 290	93.07 204	73.30 206	89.74 134	92.25 173

miper_ehance_all_eth		78.59 239	77.76 239	81.08 268	82.66 371	61.56 312	83.65 312	89.15 225	68.87 295	75.55 274	83.79 357	66.49 136	92.03 248	73.25 207	76.39 345	89.64 280

3Dnovator	76.31 5	83.38 118	82.31 132	86.59 61	87.94 208	72.94 28	90.64 68	92.14 105	77.21 63	75.47 275	92.83 97	58.56 243	94.72 115	73.24 208	92.71 81	92.13 183

v1240		78.99 228	77.78 237	82.64 229	83.21 353	63.54 273	86.62 223	90.30 175	69.74 273	77.33 231	85.68 312	57.04 259	93.76 161	73.13 209	76.92 334	90.62 233

miper_enhance_ethall		77.87 259	76.86 260	80.92 273	81.65 385	61.38 314	82.68 332	88.98 233	65.52 341	75.47 275	82.30 386	65.76 150	92.00 251	72.95 210	76.39 345	89.39 287

MG-MVS		83.41 116	83.45 109	83.28 192	92.74 71	62.28 303	88.17 164	89.50 203	75.22 123	81.49 151	92.74 103	66.75 131	95.11 94	72.85 211	91.58 101	92.45 165

EPP-MVSNet		83.40 117	83.02 117	84.57 123	90.13 114	64.47 248	92.32 35	90.73 159	74.45 150	79.35 188	91.10 151	69.05 103	95.12 92	72.78 212	87.22 181	94.13 69

test_fmvs3		63.36 412	61.82 415	67.98 430	62.51 470	46.96 451	77.37 406	74.03 437	45.24 455	67.50 386	78.79 426	12.16 474	72.98 459	72.77 213	66.02 426	83.99 409

IterMVS-LS		80.06 201	79.38 195	82.11 241	85.89 285	63.20 283	86.79 215	89.34 208	74.19 157	75.45 278	86.72 282	66.62 133	92.39 235	72.58 214	76.86 336	90.75 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tt0805		78.73 234	77.83 234	81.43 255	85.17 304	60.30 330	89.41 107	90.90 152	71.21 226	77.17 240	88.73 224	46.38 368	93.21 192	72.57 215	78.96 310	90.79 225

EI-MVSNet		80.52 189	79.98 177	82.12 239	84.28 325	63.19 284	86.41 229	88.95 236	74.18 158	78.69 197	87.54 262	66.62 133	92.43 233	72.57 215	80.57 292	90.74 229

icg_test_0407_2		78.92 231	78.93 208	78.90 315	87.13 249	63.59 269	76.58 410	89.33 209	70.51 247	77.82 220	89.03 214	61.84 197	81.38 414	72.56 217	85.56 215	91.74 191

IMVS_0407		80.61 183	79.90 180	82.75 227	87.13 249	63.59 269	85.33 266	89.33 209	70.51 247	77.82 220	89.03 214	61.84 197	92.91 211	72.56 217	85.56 215	91.74 191

IMVS_0404		77.16 275	76.42 273	79.37 306	87.13 249	63.59 269	77.12 408	89.33 209	70.51 247	66.22 407	89.03 214	50.36 330	82.78 404	72.56 217	85.56 215	91.74 191

IMVS_0403		80.80 176	80.12 175	82.87 216	87.13 249	63.59 269	85.19 267	89.33 209	70.51 247	78.49 204	89.03 214	63.26 171	93.27 187	72.56 217	85.56 215	91.74 191

SSM_0407		81.58 156	80.48 164	84.87 113	88.81 167	67.96 149	87.37 192	89.25 219	71.06 231	79.48 184	90.39 174	59.57 234	94.48 126	72.45 221	85.93 208	92.18 178

SSM_0404		81.91 146	80.84 157	85.13 101	89.24 151	68.26 137	87.84 179	89.25 219	71.06 231	80.62 168	90.39 174	59.57 234	94.65 119	72.45 221	87.19 182	92.47 164

Vis-MVSNet		83.46 115	82.80 122	85.43 90	90.25 112	68.74 121	90.30 80	90.13 181	76.33 92	80.87 164	92.89 95	61.00 218	94.20 136	72.45 221	90.97 111	93.35 117
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
LFMVS		81.82 149	81.23 149	83.57 183	91.89 82	63.43 278	89.84 87	81.85 371	77.04 70	83.21 118	93.10 88	52.26 301	93.43 181	71.98 224	89.95 130	93.85 85

v148		78.72 235	77.80 236	81.47 254	82.73 369	61.96 307	86.30 236	88.08 260	73.26 185	76.18 263	85.47 319	62.46 187	92.36 237	71.92 225	73.82 384	90.09 259

PVSNet_BlendedMVS		80.60 185	80.02 176	82.36 236	88.85 163	65.40 216	86.16 242	92.00 108	69.34 279	78.11 214	86.09 305	66.02 146	94.27 131	71.52 226	82.06 273	87.39 343

PVSNet_Blended		80.98 168	80.34 167	82.90 214	88.85 163	65.40 216	84.43 293	92.00 108	67.62 312	78.11 214	85.05 331	66.02 146	94.27 131	71.52 226	89.50 138	89.01 299

eth_miper_zixun_eth		77.92 257	76.69 267	81.61 252	83.00 361	61.98 306	83.15 325	89.20 223	69.52 276	74.86 301	84.35 344	61.76 200	92.56 226	71.50 228	72.89 392	90.28 250

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 155	82.48 2	84.60 92	93.20 87	69.35 96	95.22 88	71.39 229	90.88 114	93.07 134

FA-MVS(test-final)		80.96 169	79.91 179	84.10 150	88.30 191	65.01 228	84.55 288	90.01 184	73.25 186	79.61 181	87.57 259	58.35 245	94.72 115	71.29 230	86.25 200	92.56 157

cl____		77.72 262	76.76 264	80.58 280	82.49 375	60.48 327	83.09 327	87.87 268	69.22 284	74.38 310	85.22 326	62.10 194	91.53 275	71.09 231	75.41 364	89.73 279

DIV-MVS_self_test		77.72 262	76.76 264	80.58 280	82.48 376	60.48 327	83.09 327	87.86 269	69.22 284	74.38 310	85.24 324	62.10 194	91.53 275	71.09 231	75.40 365	89.74 278

MonoMVSNet		76.49 289	75.80 278	78.58 321	81.55 388	58.45 346	86.36 234	86.22 306	74.87 140	74.73 303	83.73 359	51.79 314	88.73 340	70.78 233	72.15 397	88.55 320

test_yl		81.17 164	80.47 165	83.24 195	89.13 156	63.62 265	86.21 240	89.95 186	72.43 202	81.78 146	89.61 197	57.50 253	93.58 166	70.75 234	86.90 187	92.52 159

DCV-MVSNet		81.17 164	80.47 165	83.24 195	89.13 156	63.62 265	86.21 240	89.95 186	72.43 202	81.78 146	89.61 197	57.50 253	93.58 166	70.75 234	86.90 187	92.52 159

VNet		82.21 140	82.41 129	81.62 250	90.82 100	60.93 319	84.47 289	89.78 190	76.36 91	84.07 104	91.88 118	64.71 158	90.26 309	70.68 236	88.89 148	93.66 97

mvs_anonymous		79.42 215	79.11 204	80.34 285	84.45 324	57.97 355	82.59 333	87.62 275	67.40 316	76.17 265	88.56 232	68.47 111	89.59 322	70.65 237	86.05 204	93.47 112

VPA-MVSNet		80.60 185	80.55 162	80.76 276	88.07 202	60.80 322	86.86 212	91.58 132	75.67 110	80.24 174	89.45 206	63.34 168	90.25 310	70.51 238	79.22 309	91.23 209

PAPM_NR		83.02 128	82.41 129	84.82 115	92.47 76	66.37 192	87.93 174	91.80 120	73.82 166	77.32 232	90.66 166	67.90 120	94.90 104	70.37 239	89.48 139	93.19 127

mamba_0408		79.37 219	77.52 246	84.93 110	88.81 167	67.96 149	65.03 463	88.66 249	70.96 235	79.48 184	89.80 189	58.69 240	94.65 119	70.35 240	85.93 208	92.18 178

SSM_04072		77.67 266	77.52 246	78.12 332	88.81 167	67.96 149	65.03 463	88.66 249	70.96 235	79.48 184	89.80 189	58.69 240	74.23 455	70.35 240	85.93 208	92.18 178

thisisatest0530		79.40 216	77.76 239	84.31 137	87.69 228	65.10 227	87.36 193	84.26 334	70.04 260	77.42 229	88.26 241	49.94 336	94.79 112	70.20 242	84.70 227	93.03 138

tttt0517		79.40 216	77.91 230	83.90 173	88.10 200	63.84 261	88.37 157	84.05 336	71.45 220	76.78 246	89.12 211	49.93 338	94.89 105	70.18 243	83.18 260	92.96 143

UniMVSNet_NR-MVSNet		81.88 147	81.54 146	82.92 213	88.46 184	63.46 276	87.13 199	92.37 86	80.19 12	78.38 207	89.14 210	71.66 64	93.05 206	70.05 244	76.46 343	92.25 173

DU-MVS		81.12 167	80.52 163	82.90 214	87.80 215	63.46 276	87.02 204	91.87 116	79.01 31	78.38 207	89.07 212	65.02 155	93.05 206	70.05 244	76.46 343	92.20 176

XVG-ACMP-BASELINE		76.11 295	74.27 307	81.62 250	83.20 354	64.67 241	83.60 315	89.75 194	69.75 271	71.85 341	87.09 275	32.78 443	92.11 246	69.99 246	80.43 294	88.09 329

GeoE		81.71 151	81.01 154	83.80 177	89.51 134	64.45 249	88.97 126	88.73 248	71.27 225	78.63 200	89.76 192	66.32 139	93.20 195	69.89 247	86.02 205	93.74 94

FIs		82.07 143	82.42 128	81.04 269	88.80 171	58.34 348	88.26 161	93.49 31	76.93 72	78.47 206	91.04 154	69.92 89	92.34 239	69.87 248	84.97 222	92.44 166

114514_t		80.68 181	79.51 192	84.20 147	94.09 42	67.27 176	89.64 96	91.11 147	58.75 415	74.08 312	90.72 163	58.10 246	95.04 99	69.70 249	89.42 140	90.30 249

Anonymous20231211		78.97 229	77.69 242	82.81 219	90.54 106	64.29 252	90.11 83	91.51 134	65.01 348	76.16 266	88.13 248	50.56 327	93.03 209	69.68 250	77.56 329	91.11 212

Patchmatch-RL test		70.24 368	67.78 381	77.61 345	77.43 429	59.57 339	71.16 438	70.33 445	62.94 374	68.65 376	72.77 451	50.62 326	85.49 381	69.58 251	66.58 424	87.77 335

UniMVSNet (Re)		81.60 155	81.11 151	83.09 202	88.38 188	64.41 250	87.60 183	93.02 50	78.42 37	78.56 202	88.16 243	69.78 90	93.26 188	69.58 251	76.49 342	91.60 196

IterMVS-SCA-FT		75.43 305	73.87 312	80.11 291	82.69 370	64.85 238	81.57 344	83.47 345	69.16 287	70.49 353	84.15 351	51.95 309	88.15 350	69.23 253	72.14 398	87.34 345

v7n		78.97 229	77.58 245	83.14 200	83.45 347	65.51 214	88.32 159	91.21 142	73.69 170	72.41 334	86.32 300	57.93 247	93.81 157	69.18 254	75.65 356	90.11 257

Anonymous20240529		80.19 200	78.89 209	84.10 150	90.60 104	64.75 240	88.95 127	90.90 152	65.97 336	80.59 169	91.17 150	49.97 335	93.73 164	69.16 255	82.70 267	93.81 89

miper_lstm_enhance		74.11 320	73.11 322	77.13 353	80.11 407	59.62 337	72.23 434	86.92 293	66.76 321	70.40 354	82.92 376	56.93 260	82.92 403	69.06 256	72.63 393	88.87 306

testdata				79.97 293	90.90 98	64.21 253		84.71 325	59.27 408	85.40 75	92.91 94	62.02 196	89.08 333	68.95 257	91.37 105	86.63 368

test1111		79.43 214	79.18 203	80.15 290	89.99 121	53.31 417	87.33 195	77.05 422	75.04 131	80.23 175	92.77 102	48.97 350	92.33 240	68.87 258	92.40 86	94.81 22

GA-MVS		76.87 280	75.17 294	81.97 245	82.75 368	62.58 294	81.44 347	86.35 305	72.16 207	74.74 302	82.89 377	46.20 373	92.02 250	68.85 259	81.09 283	91.30 208

test2506		77.30 273	76.49 270	79.74 298	90.08 116	52.02 422	87.86 178	63.10 465	74.88 138	80.16 176	92.79 100	38.29 428	92.35 238	68.74 260	92.50 84	94.86 19

ECVR-MVS		79.61 207	79.26 200	80.67 278	90.08 116	54.69 404	87.89 176	77.44 418	74.88 138	80.27 173	92.79 100	48.96 351	92.45 232	68.55 261	92.50 84	94.86 19

UGNet		80.83 172	79.59 191	84.54 124	88.04 203	68.09 144	89.42 106	88.16 257	76.95 71	76.22 261	89.46 204	49.30 345	93.94 147	68.48 262	90.31 121	91.60 196
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		81.52 159	82.02 140	80.03 292	88.42 187	55.97 389	87.95 172	93.42 34	77.10 68	77.38 230	90.98 160	69.96 88	91.79 259	68.46 263	84.50 229	92.33 169

DP-MVS Recon		83.11 127	82.09 138	86.15 70	94.44 23	70.92 76	88.79 135	92.20 98	70.53 246	79.17 190	91.03 156	64.12 163	96.03 55	68.39 264	90.14 125	91.50 201

UniMVSNet_ETH3D		79.10 225	78.24 223	81.70 249	86.85 260	60.24 331	87.28 197	88.79 241	74.25 156	76.84 243	90.53 172	49.48 341	91.56 270	67.98 265	82.15 271	93.29 119

D2MVS		74.82 312	73.21 320	79.64 302	79.81 412	62.56 296	80.34 365	87.35 281	64.37 355	68.86 374	82.66 381	46.37 369	90.10 312	67.91 266	81.24 281	86.25 371

IS-MVSNet		83.15 124	82.81 121	84.18 148	89.94 123	63.30 280	91.59 51	88.46 255	79.04 30	79.49 183	92.16 111	65.10 154	94.28 130	67.71 267	91.86 97	94.95 12

Fast-Effi-MVS+-dtu		78.02 254	76.49 270	82.62 230	83.16 357	66.96 185	86.94 208	87.45 280	72.45 199	71.49 346	84.17 350	54.79 277	91.58 267	67.61 268	80.31 295	89.30 290

PAPR		81.66 154	80.89 156	83.99 169	90.27 111	64.00 256	86.76 218	91.77 123	68.84 296	77.13 242	89.50 200	67.63 122	94.88 106	67.55 269	88.52 157	93.09 133

cascas		76.72 283	74.64 299	82.99 209	85.78 288	65.88 204	82.33 335	89.21 222	60.85 394	72.74 328	81.02 399	47.28 358	93.75 162	67.48 270	85.02 221	89.34 289

1314		76.53 285	75.30 292	80.21 289	83.93 334	62.32 302	84.66 283	88.81 240	60.23 399	70.16 359	84.07 352	55.30 271	90.73 305	67.37 271	83.21 259	87.59 340

无先验							87.48 186	88.98 233	60.00 401				94.12 140	67.28 272		88.97 302

thisisatest0515		77.33 272	75.38 289	83.18 198	85.27 303	63.80 262	82.11 338	83.27 348	65.06 346	75.91 267	83.84 355	49.54 340	94.27 131	67.24 273	86.19 201	91.48 203

原ACMM1				84.35 134	93.01 66	68.79 117		92.44 82	63.96 364	81.09 158	91.57 134	66.06 145	95.45 75	67.19 274	94.82 50	88.81 309

Baseline_NR-MVSNet		78.15 250	78.33 221	77.61 345	85.79 287	56.21 387	86.78 216	85.76 314	73.60 173	77.93 219	87.57 259	65.02 155	88.99 334	67.14 275	75.33 367	87.63 337

TranMVSNet+NR-MVSNet		80.84 171	80.31 168	82.42 234	87.85 212	62.33 301	87.74 181	91.33 139	80.55 9	77.99 218	89.86 185	65.23 153	92.62 221	67.05 276	75.24 370	92.30 171

Fast-Effi-MVS+		80.81 173	79.92 178	83.47 184	88.85 163	64.51 245	85.53 262	89.39 207	70.79 238	78.49 204	85.06 330	67.54 123	93.58 166	67.03 277	86.58 193	92.32 170

VPNet		78.69 236	78.66 212	78.76 317	88.31 190	55.72 393	84.45 292	86.63 299	76.79 76	78.26 210	90.55 171	59.30 237	89.70 321	66.63 278	77.05 333	90.88 222

PM-MVS		66.41 400	64.14 403	73.20 395	73.92 446	56.45 380	78.97 385	64.96 462	63.88 365	64.72 416	80.24 410	19.84 466	83.44 400	66.24 279	64.52 431	79.71 443

test-LLR		72.94 340	72.43 329	74.48 380	81.35 393	58.04 353	78.38 393	77.46 416	66.66 323	69.95 363	79.00 423	48.06 354	79.24 422	66.13 280	84.83 224	86.15 374

test-mter		71.41 354	70.39 355	74.48 380	81.35 393	58.04 353	78.38 393	77.46 416	60.32 398	69.95 363	79.00 423	36.08 437	79.24 422	66.13 280	84.83 224	86.15 374

MVS		78.19 249	76.99 258	81.78 247	85.66 290	66.99 182	84.66 283	90.47 166	55.08 436	72.02 340	85.27 323	63.83 166	94.11 141	66.10 282	89.80 133	84.24 405

NR-MVSNet		80.23 198	79.38 195	82.78 224	87.80 215	63.34 279	86.31 235	91.09 148	79.01 31	72.17 338	89.07 212	67.20 127	92.81 218	66.08 283	75.65 356	92.20 176

CVMVSNet		72.99 339	72.58 328	74.25 384	84.28 325	50.85 436	86.41 229	83.45 346	44.56 456	73.23 323	87.54 262	49.38 343	85.70 377	65.90 284	78.44 315	86.19 373

IterMVS		74.29 316	72.94 324	78.35 328	81.53 389	63.49 275	81.58 343	82.49 363	68.06 309	69.99 362	83.69 361	51.66 316	85.54 380	65.85 285	71.64 401	86.01 378
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 317	72.42 330	79.80 297	83.76 339	59.59 338	85.92 248	86.64 298	66.39 330	66.96 394	87.58 258	39.46 419	91.60 266	65.76 286	69.27 412	88.22 326

tpmrst		72.39 343	72.13 333	73.18 396	80.54 402	49.91 440	79.91 373	79.08 406	63.11 370	71.69 343	79.95 413	55.32 270	82.77 405	65.66 287	73.89 382	86.87 360

MAR-MVS		81.84 148	80.70 158	85.27 94	91.32 89	71.53 58	89.82 88	90.92 151	69.77 270	78.50 203	86.21 301	62.36 189	94.52 123	65.36 288	92.05 93	89.77 277
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		78.25 245	77.01 256	81.99 244	91.03 94	60.67 324	84.77 280	83.90 338	70.65 245	80.00 177	91.20 148	41.08 412	91.43 281	65.21 289	85.26 220	93.85 85

ab-mvs		79.51 210	78.97 207	81.14 266	88.46 184	60.91 320	83.84 307	89.24 221	70.36 252	79.03 191	88.87 222	63.23 173	90.21 311	65.12 290	82.57 268	92.28 172

IB-MVS	68.01 15	75.85 299	73.36 319	83.31 191	84.76 316	66.03 197	83.38 320	85.06 322	70.21 259	69.40 369	81.05 398	45.76 378	94.66 118	65.10 291	75.49 359	89.25 291
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		79.49 211	79.22 202	80.27 287	88.79 172	58.35 347	85.06 274	88.61 253	78.56 35	77.65 225	88.34 237	63.81 167	90.66 306	64.98 292	77.22 331	91.80 190

CostFormer		75.24 309	73.90 311	79.27 308	82.65 372	58.27 349	80.80 353	82.73 362	61.57 389	75.33 287	83.13 372	55.52 269	91.07 296	64.98 292	78.34 320	88.45 321

API-MVS		81.99 145	81.23 149	84.26 145	90.94 97	70.18 91	91.10 63	89.32 213	71.51 219	78.66 199	88.28 239	65.26 152	95.10 97	64.74 294	91.23 107	87.51 341

新几何1				83.42 187	93.13 60	70.71 80		85.48 317	57.43 426	81.80 145	91.98 115	63.28 169	92.27 241	64.60 295	92.99 76	87.27 348

testing91		76.54 284	75.66 283	79.18 311	88.43 186	55.89 390	81.08 350	83.00 356	73.76 168	75.34 283	84.29 345	46.20 373	90.07 313	64.33 296	84.50 229	91.58 198

testing99		76.09 296	75.12 295	79.00 312	88.16 195	55.50 396	80.79 354	81.40 376	73.30 184	75.17 291	84.27 348	44.48 388	90.02 314	64.28 297	84.22 238	91.48 203

pm-mvs1		77.25 274	76.68 268	78.93 314	84.22 327	58.62 345	86.41 229	88.36 256	71.37 221	73.31 321	88.01 249	61.22 214	89.15 332	64.24 298	73.01 391	89.03 298

TESTMET0.1,1		69.89 373	69.00 365	72.55 401	79.27 421	56.85 373	78.38 393	74.71 435	57.64 423	68.09 381	77.19 436	37.75 430	76.70 435	63.92 299	84.09 239	84.10 408

QAPM		80.88 170	79.50 193	85.03 104	88.01 206	68.97 114	91.59 51	92.00 108	66.63 328	75.15 293	92.16 111	57.70 250	95.45 75	63.52 300	88.76 152	90.66 232

baseline2		75.70 300	73.83 313	81.30 260	83.26 351	61.79 310	82.57 334	80.65 383	66.81 319	66.88 395	83.42 367	57.86 249	92.19 244	63.47 301	79.57 302	89.91 270

LCM-MVSNet-Re		77.05 276	76.94 259	77.36 349	87.20 246	51.60 429	80.06 369	80.46 388	75.20 126	67.69 384	86.72 282	62.48 186	88.98 335	63.44 302	89.25 141	91.51 200

gm-plane-assit					81.40 391	53.83 412			62.72 379		80.94 401		92.39 235	63.40 303

baseline1		76.98 278	76.75 266	77.66 343	88.13 198	55.66 394	85.12 271	81.89 369	73.04 192	76.79 245	88.90 220	62.43 188	87.78 356	63.30 304	71.18 404	89.55 283

AdaColmap		80.58 188	79.42 194	84.06 159	93.09 63	68.91 115	89.36 110	88.97 235	69.27 281	75.70 271	89.69 193	57.20 258	95.77 64	63.06 305	88.41 160	87.50 342

test_vis1_rt		60.28 417	58.42 420	65.84 435	67.25 464	55.60 395	70.44 443	60.94 468	44.33 457	59.00 443	66.64 458	24.91 457	68.67 465	62.80 306	69.48 410	73.25 454

GBi-Net		78.40 242	77.40 249	81.40 257	87.60 231	63.01 286	88.39 154	89.28 215	71.63 214	75.34 283	87.28 266	54.80 274	91.11 290	62.72 307	79.57 302	90.09 259

test1		78.40 242	77.40 249	81.40 257	87.60 231	63.01 286	88.39 154	89.28 215	71.63 214	75.34 283	87.28 266	54.80 274	91.11 290	62.72 307	79.57 302	90.09 259

FMVSNet3		77.88 258	76.85 261	80.97 272	86.84 261	62.36 300	86.52 226	88.77 243	71.13 227	75.34 283	86.66 288	54.07 284	91.10 293	62.72 307	79.57 302	89.45 285

CMPMVS	51.72 21	70.19 369	68.16 371	76.28 358	73.15 454	57.55 365	79.47 376	83.92 337	48.02 452	56.48 452	84.81 335	43.13 397	86.42 370	62.67 310	81.81 277	84.89 398
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
sd_testset		77.70 264	77.40 249	78.60 320	89.03 161	60.02 333	79.00 384	85.83 313	75.19 127	76.61 252	89.98 183	54.81 273	85.46 382	62.63 311	83.55 251	90.33 247

FMVSNet2		78.20 248	77.21 253	81.20 264	87.60 231	62.89 292	87.47 187	89.02 231	71.63 214	75.29 289	87.28 266	54.80 274	91.10 293	62.38 312	79.38 306	89.61 281

testdata2													91.01 297	62.37 313

testing11		75.14 310	74.01 308	78.53 324	88.16 195	56.38 383	80.74 357	80.42 390	70.67 241	72.69 331	83.72 360	43.61 395	89.86 316	62.29 314	83.76 244	89.36 288

CP-MVSNet		78.22 246	78.34 220	77.84 339	87.83 214	54.54 406	87.94 173	91.17 144	77.65 46	73.48 320	88.49 233	62.24 192	88.43 347	62.19 315	74.07 379	90.55 237

XXY-MVS		75.41 306	75.56 284	74.96 374	83.59 344	57.82 359	80.59 360	83.87 339	66.54 329	74.93 300	88.31 238	63.24 172	80.09 420	62.16 316	76.85 337	86.97 359

pmmvs6		74.69 313	73.39 317	78.61 319	81.38 392	57.48 366	86.64 222	87.95 266	64.99 349	70.18 357	86.61 289	50.43 329	89.52 323	62.12 317	70.18 409	88.83 308

1112_ss		77.40 271	76.43 272	80.32 286	89.11 160	60.41 329	83.65 312	87.72 274	62.13 385	73.05 325	86.72 282	62.58 185	89.97 315	62.11 318	80.80 288	90.59 236

PS-CasMVS		78.01 255	78.09 226	77.77 341	87.71 224	54.39 408	88.02 169	91.22 141	77.50 54	73.26 322	88.64 228	60.73 220	88.41 348	61.88 319	73.88 383	90.53 238

CDS-MVSNet		79.07 226	77.70 241	83.17 199	87.60 231	68.23 141	84.40 296	86.20 307	67.49 314	76.36 258	86.54 294	61.54 204	90.79 301	61.86 320	87.33 179	90.49 240
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OpenMVS	72.83 10	79.77 205	78.33 221	84.09 154	85.17 304	69.91 93	90.57 69	90.97 150	66.70 322	72.17 338	91.91 116	54.70 278	93.96 144	61.81 321	90.95 112	88.41 323

K. test v3		71.19 355	68.51 367	79.21 310	83.04 360	57.78 361	84.35 297	76.91 423	72.90 195	62.99 429	82.86 378	39.27 420	91.09 295	61.65 322	52.66 456	88.75 312

CHOSEN 1792x2688		77.63 267	75.69 280	83.44 186	89.98 122	68.58 129	78.70 389	87.50 278	56.38 431	75.80 270	86.84 278	58.67 242	91.40 282	61.58 323	85.75 213	90.34 246

PCF-MVS	73.52 7	80.38 192	78.84 210	85.01 105	87.71 224	68.99 113	83.65 312	91.46 138	63.00 372	77.77 224	90.28 177	66.10 143	95.09 98	61.40 324	88.22 162	90.94 221
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HY-MVS	69.67 12	77.95 256	77.15 254	80.36 284	87.57 235	60.21 332	83.37 321	87.78 272	66.11 332	75.37 282	87.06 277	63.27 170	90.48 308	61.38 325	82.43 269	90.40 244

HyFIR lowres test		77.53 268	75.40 288	83.94 172	89.59 130	66.62 188	80.36 364	88.64 252	56.29 432	76.45 255	85.17 327	57.64 251	93.28 185	61.34 326	83.10 261	91.91 187

PMMVS		69.34 377	68.67 366	71.35 411	75.67 438	62.03 305	75.17 420	73.46 438	50.00 449	68.68 375	79.05 421	52.07 307	78.13 427	61.16 327	82.77 264	73.90 453

FMVSNet1		77.44 269	76.12 277	81.40 257	86.81 262	63.01 286	88.39 154	89.28 215	70.49 251	74.39 309	87.28 266	49.06 349	91.11 290	60.91 328	78.52 313	90.09 259

sss		73.60 327	73.64 315	73.51 391	82.80 367	55.01 402	76.12 412	81.69 372	62.47 381	74.68 304	85.85 309	57.32 255	78.11 428	60.86 329	80.93 284	87.39 343

Test_1112_low_res		76.40 291	75.44 286	79.27 308	89.28 149	58.09 351	81.69 342	87.07 288	59.53 406	72.48 333	86.67 287	61.30 211	89.33 326	60.81 330	80.15 297	90.41 243

sc_t1		72.19 348	69.51 360	80.23 288	84.81 314	61.09 317	84.68 282	80.22 394	60.70 395	71.27 347	83.58 364	36.59 434	89.24 329	60.41 331	63.31 434	90.37 245

BH-untuned		79.47 212	78.60 213	82.05 242	89.19 154	65.91 203	86.07 244	88.52 254	72.18 205	75.42 279	87.69 256	61.15 215	93.54 172	60.38 332	86.83 190	86.70 365

WTY-MVS		75.65 301	75.68 281	75.57 365	86.40 274	56.82 374	77.92 402	82.40 364	65.10 345	76.18 263	87.72 254	63.13 178	80.90 417	60.31 333	81.96 274	89.00 301

pmmvs4		74.03 323	71.91 334	80.39 283	81.96 381	68.32 135	81.45 346	82.14 366	59.32 407	69.87 365	85.13 328	52.40 299	88.13 351	60.21 334	74.74 375	84.73 401

PEN-MVS		77.73 261	77.69 242	77.84 339	87.07 257	53.91 411	87.91 175	91.18 143	77.56 51	73.14 324	88.82 223	61.23 213	89.17 331	59.95 335	72.37 394	90.43 242

CR-MVSNet		73.37 330	71.27 344	79.67 301	81.32 395	65.19 222	75.92 414	80.30 392	59.92 402	72.73 329	81.19 396	52.50 297	86.69 365	59.84 336	77.71 325	87.11 354

mvs5depth		69.45 376	67.45 387	75.46 369	73.93 445	55.83 391	79.19 381	83.23 349	66.89 318	71.63 344	83.32 368	33.69 442	85.09 385	59.81 337	55.34 453	85.46 387

lessismore_v0				78.97 313	81.01 398	57.15 370		65.99 458		61.16 435	82.82 379	39.12 422	91.34 284	59.67 338	46.92 463	88.43 322

CNLPA		78.08 251	76.79 263	81.97 245	90.40 109	71.07 70	87.59 184	84.55 328	66.03 335	72.38 335	89.64 196	57.56 252	86.04 374	59.61 339	83.35 256	88.79 310

BH-RMVSNet		79.61 207	78.44 217	83.14 200	89.38 143	65.93 202	84.95 277	87.15 287	73.56 174	78.19 212	89.79 191	56.67 263	93.36 183	59.53 340	86.74 191	90.13 255

FE-MVSNET2		72.88 341	71.28 343	77.67 342	78.30 426	57.78 361	84.43 293	88.92 238	69.56 274	64.61 417	81.67 393	46.73 366	88.54 346	59.33 341	67.99 418	86.69 366

MS-PatchMatch		73.83 324	72.67 326	77.30 351	83.87 336	66.02 198	81.82 339	84.66 326	61.37 392	68.61 377	82.82 379	47.29 357	88.21 349	59.27 342	84.32 236	77.68 447

test_post1							78.90 387				5.43 481	48.81 353	85.44 383	59.25 343

SCA		74.22 318	72.33 331	79.91 294	84.05 332	62.17 304	79.96 372	79.29 404	66.30 331	72.38 335	80.13 411	51.95 309	88.60 344	59.25 343	77.67 328	88.96 303

FE-MVS		77.78 260	75.68 281	84.08 155	88.09 201	66.00 200	83.13 326	87.79 271	68.42 305	78.01 217	85.23 325	45.50 382	95.12 92	59.11 345	85.83 212	91.11 212

SixPastTwentyTwo		73.37 330	71.26 345	79.70 299	85.08 309	57.89 357	85.57 256	83.56 343	71.03 233	65.66 409	85.88 307	42.10 405	92.57 225	59.11 345	63.34 433	88.65 316

WR-MVS_H		78.51 241	78.49 215	78.56 322	88.02 204	56.38 383	88.43 151	92.67 72	77.14 65	73.89 314	87.55 261	66.25 140	89.24 329	58.92 347	73.55 386	90.06 263

PLC	70.83 11	78.05 253	76.37 275	83.08 204	91.88 83	67.80 156	88.19 163	89.46 204	64.33 356	69.87 365	88.38 236	53.66 288	93.58 166	58.86 348	82.73 265	87.86 333
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
RPSCF		73.23 335	71.46 339	78.54 323	82.50 374	59.85 334	82.18 337	82.84 361	58.96 411	71.15 350	89.41 208	45.48 383	84.77 389	58.82 349	71.83 400	91.02 218

EU-MVSNet		68.53 385	67.61 384	71.31 412	78.51 425	47.01 450	84.47 289	84.27 333	42.27 459	66.44 405	84.79 336	40.44 415	83.76 395	58.76 350	68.54 417	83.17 417

pmmvs-eth3d		70.50 365	67.83 379	78.52 325	77.37 430	66.18 195	81.82 339	81.51 374	58.90 412	63.90 424	80.42 406	42.69 400	86.28 371	58.56 351	65.30 429	83.11 419

TAMVS		78.89 232	77.51 248	83.03 207	87.80 215	67.79 157	84.72 281	85.05 323	67.63 311	76.75 247	87.70 255	62.25 191	90.82 300	58.53 352	87.13 184	90.49 240

WBMVS		73.43 329	72.81 325	75.28 371	87.91 209	50.99 435	78.59 392	81.31 378	65.51 343	74.47 308	84.83 334	46.39 367	86.68 366	58.41 353	77.86 323	88.17 328

ACMH+	68.96 14	76.01 297	74.01 308	82.03 243	88.60 179	65.31 220	88.86 130	87.55 276	70.25 258	67.75 383	87.47 264	41.27 410	93.19 197	58.37 354	75.94 353	87.60 338

tpm		72.37 345	71.71 336	74.35 382	82.19 379	52.00 423	79.22 380	77.29 420	64.56 352	72.95 327	83.68 362	51.35 317	83.26 402	58.33 355	75.80 354	87.81 334

BH-w/o		78.21 247	77.33 252	80.84 274	88.81 167	65.13 224	84.87 278	87.85 270	69.75 271	74.52 307	84.74 337	61.34 210	93.11 202	58.24 356	85.84 211	84.27 404

Vis-MVSNet (Re-imp)		78.36 244	78.45 216	78.07 334	88.64 178	51.78 428	86.70 219	79.63 400	74.14 159	75.11 294	90.83 162	61.29 212	89.75 319	58.10 357	91.60 99	92.69 153

FE-MVSNET1		71.98 351	70.01 358	77.91 336	77.16 431	58.13 350	85.61 254	88.78 242	68.62 300	63.35 426	81.28 395	39.62 418	88.61 343	58.02 358	67.67 419	87.00 357

MVP-Stereo		76.12 294	74.46 304	81.13 267	85.37 300	69.79 95	84.42 295	87.95 266	65.03 347	67.46 387	85.33 322	53.28 293	91.73 263	58.01 359	83.27 258	81.85 432
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ambc				75.24 372	73.16 453	50.51 438	63.05 468	87.47 279		64.28 419	77.81 433	17.80 468	89.73 320	57.88 360	60.64 442	85.49 386

TR-MVS		77.44 269	76.18 276	81.20 264	88.24 192	63.24 281	84.61 286	86.40 303	67.55 313	77.81 222	86.48 296	54.10 283	93.15 199	57.75 361	82.72 266	87.20 349

F-COLMAP		76.38 292	74.33 306	82.50 233	89.28 149	66.95 186	88.41 153	89.03 230	64.05 361	66.83 396	88.61 229	46.78 364	92.89 212	57.48 362	78.55 312	87.67 336

EG-PatchMatch MVS		74.04 321	71.82 335	80.71 277	84.92 312	67.42 168	85.86 250	88.08 260	66.04 334	64.22 420	83.85 354	35.10 439	92.56 226	57.44 363	80.83 287	82.16 430

PatchmatchNet		73.12 336	71.33 342	78.49 326	83.18 355	60.85 321	79.63 374	78.57 409	64.13 357	71.73 342	79.81 416	51.20 320	85.97 375	57.40 364	76.36 350	88.66 315
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
DTE-MVSNet		76.99 277	76.80 262	77.54 348	86.24 276	53.06 420	87.52 185	90.66 160	77.08 69	72.50 332	88.67 227	60.48 228	89.52 323	57.33 365	70.74 406	90.05 264

UnsupCasMVSNet_eth		67.33 392	65.99 396	71.37 409	73.48 450	51.47 431	75.16 421	85.19 319	65.20 344	60.78 436	80.93 403	42.35 401	77.20 432	57.12 366	53.69 455	85.44 388

pmmvs5		71.55 353	70.20 357	75.61 364	77.83 427	56.39 382	81.74 341	80.89 379	57.76 422	67.46 387	84.49 338	49.26 346	85.32 384	57.08 367	75.29 368	85.11 395

testing3-2		75.12 311	75.19 293	74.91 375	90.40 109	45.09 458	80.29 366	78.42 410	78.37 40	76.54 254	87.75 253	44.36 389	87.28 362	57.04 368	83.49 253	92.37 167

Anonymous20240521		68.80 381	67.22 390	73.55 390	74.33 443	54.11 409	83.18 324	85.61 315	58.15 418	61.68 433	80.94 401	30.71 449	81.27 415	57.00 369	73.34 390	85.28 390

mvsany_test1		62.30 414	61.26 418	65.41 436	69.52 460	54.86 403	66.86 455	49.78 476	46.65 453	68.50 379	83.21 370	49.15 347	66.28 468	56.93 370	60.77 441	75.11 452

TransMVSNet (Re)		75.39 308	74.56 301	77.86 338	85.50 297	57.10 371	86.78 216	86.09 310	72.17 206	71.53 345	87.34 265	63.01 179	89.31 327	56.84 371	61.83 438	87.17 350

tt0320-xc		70.11 370	67.45 387	78.07 334	85.33 301	59.51 340	83.28 322	78.96 407	58.77 413	67.10 393	80.28 409	36.73 433	87.42 360	56.83 372	59.77 445	87.29 347

test_vis3_rt		49.26 434	47.02 436	56.00 447	54.30 476	45.27 457	66.76 457	48.08 477	36.83 466	44.38 465	53.20 470	7.17 481	64.07 470	56.77 373	55.66 450	58.65 466

EPMVS		69.02 379	68.16 371	71.59 407	79.61 416	49.80 442	77.40 405	66.93 456	62.82 377	70.01 360	79.05 421	45.79 377	77.86 430	56.58 374	75.26 369	87.13 353

KD-MVS_self_test		68.81 380	67.59 385	72.46 403	74.29 444	45.45 453	77.93 401	87.00 289	63.12 369	63.99 423	78.99 425	42.32 402	84.77 389	56.55 375	64.09 432	87.16 352

tpm2		73.26 334	71.46 339	78.63 318	83.34 349	56.71 377	80.65 359	80.40 391	56.63 430	73.55 319	82.02 391	51.80 313	91.24 287	56.35 376	78.42 318	87.95 330

LTVRE_ROB	69.57 13	76.25 293	74.54 302	81.41 256	88.60 179	64.38 251	79.24 379	89.12 228	70.76 240	69.79 367	87.86 252	49.09 348	93.20 195	56.21 377	80.16 296	86.65 367
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	75.89 298	73.93 310	81.77 248	88.71 176	66.61 189	88.62 145	89.01 232	69.81 267	66.78 397	86.70 286	41.95 407	91.51 277	55.64 378	78.14 321	87.17 350
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		66.51 399	64.71 401	71.90 405	81.45 390	63.52 274	57.98 470	68.95 452	53.57 439	62.59 431	76.70 437	46.22 372	75.29 451	55.25 379	79.68 301	76.88 449

tt0320		70.49 366	68.03 374	77.89 337	84.78 315	59.12 342	83.55 316	80.44 389	58.13 419	67.43 389	80.41 407	39.26 421	87.54 359	55.12 380	63.18 435	86.99 358

UBG		73.08 337	72.27 332	75.51 367	88.02 204	51.29 433	78.35 396	77.38 419	65.52 341	73.87 315	82.36 384	45.55 380	86.48 369	55.02 381	84.39 235	88.75 312

EPNet_dtu		75.46 304	74.86 296	77.23 352	82.57 373	54.60 405	86.89 210	83.09 353	71.64 213	66.25 406	85.86 308	55.99 266	88.04 352	54.92 382	86.55 194	89.05 297
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
mvsany_test3		53.99 425	51.45 430	61.61 441	55.51 475	44.74 460	63.52 466	45.41 480	43.69 458	58.11 447	76.45 439	17.99 467	63.76 471	54.77 383	47.59 462	76.34 450

PVSNet	64.34 18	72.08 350	70.87 349	75.69 363	86.21 277	56.44 381	74.37 428	80.73 382	62.06 386	70.17 358	82.23 388	42.86 399	83.31 401	54.77 383	84.45 233	87.32 346

ITE_SJBPF				78.22 329	81.77 384	60.57 325		83.30 347	69.25 283	67.54 385	87.20 271	36.33 436	87.28 362	54.34 385	74.62 376	86.80 362

SSC-MVS3.2		73.35 333	73.39 317	73.23 392	85.30 302	49.01 443	74.58 427	81.57 373	75.21 125	73.68 317	85.58 316	52.53 295	82.05 409	54.33 386	77.69 327	88.63 317

MDTV_nov1_ep13_2view						37.79 472	75.16 421		55.10 435	66.53 401		49.34 344		53.98 387		87.94 331

gg-mvs-nofinetune		69.95 372	67.96 375	75.94 360	83.07 358	54.51 407	77.23 407	70.29 446	63.11 370	70.32 355	62.33 460	43.62 394	88.69 341	53.88 388	87.76 172	84.62 402

PatchMatch-RL		72.38 344	70.90 348	76.80 356	88.60 179	67.38 171	79.53 375	76.17 428	62.75 378	69.36 370	82.00 392	45.51 381	84.89 388	53.62 389	80.58 291	78.12 446

test_f		52.09 430	50.82 431	55.90 448	53.82 478	42.31 467	59.42 469	58.31 472	36.45 467	56.12 454	70.96 455	12.18 473	57.79 474	53.51 390	56.57 449	67.60 459

Patchmtry		70.74 361	69.16 364	75.49 368	80.72 399	54.07 410	74.94 425	80.30 392	58.34 416	70.01 360	81.19 396	52.50 297	86.54 367	53.37 391	71.09 405	85.87 383

USDC		70.33 367	68.37 368	76.21 359	80.60 401	56.23 386	79.19 381	86.49 301	60.89 393	61.29 434	85.47 319	31.78 446	89.47 325	53.37 391	76.21 351	82.94 423

LF4IMVS		64.02 410	62.19 414	69.50 420	70.90 459	53.29 418	76.13 411	77.18 421	52.65 442	58.59 444	80.98 400	23.55 461	76.52 437	53.06 393	66.66 423	78.68 445

PAPM		77.68 265	76.40 274	81.51 253	87.29 245	61.85 308	83.78 308	89.59 200	64.74 350	71.23 348	88.70 225	62.59 184	93.66 165	52.66 394	87.03 186	89.01 299

dmvs_re		71.14 356	70.58 350	72.80 399	81.96 381	59.68 336	75.60 418	79.34 403	68.55 301	69.27 372	80.72 404	49.42 342	76.54 436	52.56 395	77.79 324	82.19 429

CL-MVSNet_self_test		72.37 345	71.46 339	75.09 373	79.49 418	53.53 413	80.76 356	85.01 324	69.12 288	70.51 352	82.05 390	57.92 248	84.13 393	52.27 396	66.00 427	87.60 338

tpm cat1		70.57 363	68.31 369	77.35 350	82.41 377	57.95 356	78.08 398	80.22 394	52.04 443	68.54 378	77.66 434	52.00 308	87.84 355	51.77 397	72.07 399	86.25 371

our_test_3		69.14 378	67.00 391	75.57 365	79.80 413	58.80 343	77.96 400	77.81 413	59.55 405	62.90 430	78.25 430	47.43 356	83.97 394	51.71 398	67.58 421	83.93 410

MDTV_nov1_ep13			69.97 359		83.18 355	53.48 414	77.10 409	80.18 396	60.45 396	69.33 371	80.44 405	48.89 352	86.90 364	51.60 399	78.51 314

myMVS_eth3d28		73.62 326	73.53 316	73.90 388	88.20 193	47.41 448	78.06 399	79.37 402	74.29 155	73.98 313	84.29 345	44.67 385	83.54 398	51.47 400	87.39 178	90.74 229

JIA-IIPM		66.32 401	62.82 413	76.82 355	77.09 432	61.72 311	65.34 461	75.38 429	58.04 421	64.51 418	62.32 461	42.05 406	86.51 368	51.45 401	69.22 413	82.21 428

testing222		74.04 321	72.66 327	78.19 330	87.89 210	55.36 397	81.06 351	79.20 405	71.30 224	74.65 305	83.57 365	39.11 423	88.67 342	51.43 402	85.75 213	90.53 238

MSDG		73.36 332	70.99 347	80.49 282	84.51 323	65.80 207	80.71 358	86.13 309	65.70 338	65.46 410	83.74 358	44.60 386	90.91 299	51.13 403	76.89 335	84.74 400

PatchT		68.46 386	67.85 377	70.29 417	80.70 400	43.93 461	72.47 433	74.88 432	60.15 400	70.55 351	76.57 438	49.94 336	81.59 411	50.58 404	74.83 374	85.34 389

GG-mvs-BLEND				75.38 370	81.59 387	55.80 392	79.32 378	69.63 448		67.19 391	73.67 449	43.24 396	88.90 339	50.41 405	84.50 229	81.45 434

KD-MVS_2432*1600		66.22 402	63.89 405	73.21 393	75.47 441	53.42 415	70.76 441	84.35 330	64.10 359	66.52 402	78.52 427	34.55 440	84.98 386	50.40 406	50.33 460	81.23 435

miper_refine_blended		66.22 402	63.89 405	73.21 393	75.47 441	53.42 415	70.76 441	84.35 330	64.10 359	66.52 402	78.52 427	34.55 440	84.98 386	50.40 406	50.33 460	81.23 435

AllTest		70.96 358	68.09 373	79.58 303	85.15 306	63.62 265	84.58 287	79.83 397	62.31 382	60.32 439	86.73 280	32.02 444	88.96 337	50.28 408	71.57 402	86.15 374

TestCases				79.58 303	85.15 306	63.62 265		79.83 397	62.31 382	60.32 439	86.73 280	32.02 444	88.96 337	50.28 408	71.57 402	86.15 374

TAPA-MVS	73.13 9	79.15 223	77.94 229	82.79 223	89.59 130	62.99 290	88.16 165	91.51 134	65.77 337	77.14 241	91.09 152	60.91 219	93.21 192	50.26 410	87.05 185	92.17 181
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
YYNet1		65.03 406	62.91 411	71.38 408	75.85 437	56.60 379	69.12 449	74.66 436	57.28 427	54.12 455	77.87 432	45.85 376	74.48 453	49.95 411	61.52 440	83.05 420

MDA-MVSNet_test_wron		65.03 406	62.92 410	71.37 409	75.93 434	56.73 375	69.09 450	74.73 434	57.28 427	54.03 456	77.89 431	45.88 375	74.39 454	49.89 412	61.55 439	82.99 422

tpmvs		71.09 357	69.29 362	76.49 357	82.04 380	56.04 388	78.92 386	81.37 377	64.05 361	67.18 392	78.28 429	49.74 339	89.77 318	49.67 413	72.37 394	83.67 413

SD_0403		74.65 314	74.77 298	74.29 383	86.20 278	47.42 447	83.71 310	85.12 320	69.30 280	68.50 379	87.95 251	59.40 236	86.05 373	49.38 414	83.35 256	89.40 286

ppachtmachnet_test		70.04 371	67.34 389	78.14 331	79.80 413	61.13 315	79.19 381	80.59 384	59.16 409	65.27 412	79.29 420	46.75 365	87.29 361	49.33 415	66.72 422	86.00 380

UnsupCasMVSNet_bld		63.70 411	61.53 417	70.21 418	73.69 448	51.39 432	72.82 432	81.89 369	55.63 434	57.81 448	71.80 453	38.67 425	78.61 425	49.26 416	52.21 458	80.63 439

UWE-MVS		72.13 349	71.49 338	74.03 386	86.66 268	47.70 445	81.40 348	76.89 424	63.60 367	75.59 272	84.22 349	39.94 417	85.62 379	48.98 417	86.13 203	88.77 311

dp		66.80 396	65.43 397	70.90 416	79.74 415	48.82 444	75.12 423	74.77 433	59.61 404	64.08 422	77.23 435	42.89 398	80.72 418	48.86 418	66.58 424	83.16 418

FMVSNet5		69.50 375	67.96 375	74.15 385	82.97 364	55.35 398	80.01 371	82.12 367	62.56 380	63.02 427	81.53 394	36.92 432	81.92 410	48.42 419	74.06 380	85.17 394

thres100view900		76.50 286	75.55 285	79.33 307	89.52 133	56.99 372	85.83 252	83.23 349	73.94 163	76.32 259	87.12 274	51.89 311	91.95 253	48.33 420	83.75 245	89.07 292

tfpn200view9		76.42 290	75.37 290	79.55 305	89.13 156	57.65 363	85.17 268	83.60 341	73.41 180	76.45 255	86.39 298	52.12 303	91.95 253	48.33 420	83.75 245	89.07 292

thres400		76.50 286	75.37 290	79.86 295	89.13 156	57.65 363	85.17 268	83.60 341	73.41 180	76.45 255	86.39 298	52.12 303	91.95 253	48.33 420	83.75 245	90.00 265

LCM-MVSNet		54.25 424	49.68 434	67.97 431	53.73 479	45.28 456	66.85 456	80.78 381	35.96 468	39.45 469	62.23 462	8.70 478	78.06 429	48.24 423	51.20 459	80.57 440

RPMNet		73.51 328	70.49 352	82.58 232	81.32 395	65.19 222	75.92 414	92.27 89	57.60 424	72.73 329	76.45 439	52.30 300	95.43 77	48.14 424	77.71 325	87.11 354

thres600view7		76.50 286	75.44 286	79.68 300	89.40 141	57.16 369	85.53 262	83.23 349	73.79 167	76.26 260	87.09 275	51.89 311	91.89 256	48.05 425	83.72 248	90.00 265

TDRefinement		67.49 390	64.34 402	76.92 354	73.47 451	61.07 318	84.86 279	82.98 357	59.77 403	58.30 446	85.13 328	26.06 454	87.89 354	47.92 426	60.59 443	81.81 433

thres200		75.55 302	74.47 303	78.82 316	87.78 218	57.85 358	83.07 329	83.51 344	72.44 201	75.84 269	84.42 340	52.08 306	91.75 261	47.41 427	83.64 250	86.86 361

PVSNet_0	57.27 20	61.67 416	59.27 419	68.85 424	79.61 416	57.44 367	68.01 451	73.44 439	55.93 433	58.54 445	70.41 456	44.58 387	77.55 431	47.01 428	35.91 468	71.55 456

DP-MVS		76.78 282	74.57 300	83.42 187	93.29 52	69.46 104	88.55 149	83.70 340	63.98 363	70.20 356	88.89 221	54.01 286	94.80 111	46.66 429	81.88 276	86.01 378

COLMAP_ROB	66.92 17	73.01 338	70.41 354	80.81 275	87.13 249	65.63 211	88.30 160	84.19 335	62.96 373	63.80 425	87.69 256	38.04 429	92.56 226	46.66 429	74.91 373	84.24 405
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet		70.69 362	69.30 361	74.88 376	84.52 322	56.35 385	75.87 416	79.42 401	64.59 351	67.76 382	82.41 383	41.10 411	81.54 412	46.64 431	81.34 279	86.75 364

LS3D		76.95 279	74.82 297	83.37 190	90.45 107	67.36 172	89.15 120	86.94 291	61.87 388	69.52 368	90.61 169	51.71 315	94.53 122	46.38 432	86.71 192	88.21 327

ETVMVS		72.25 347	71.05 346	75.84 361	87.77 220	51.91 425	79.39 377	74.98 431	69.26 282	73.71 316	82.95 375	40.82 414	86.14 372	46.17 433	84.43 234	89.47 284

MDA-MVSNet-bldmvs		66.68 397	63.66 407	75.75 362	79.28 420	60.56 326	73.92 430	78.35 411	64.43 353	50.13 461	79.87 415	44.02 392	83.67 396	46.10 434	56.86 447	83.03 421

new-patchmatchnet		61.73 415	61.73 416	61.70 440	72.74 456	24.50 483	69.16 448	78.03 412	61.40 390	56.72 451	75.53 445	38.42 426	76.48 438	45.95 435	57.67 446	84.13 407

WB-MVSnew		71.96 352	71.65 337	72.89 398	84.67 321	51.88 426	82.29 336	77.57 415	62.31 382	73.67 318	83.00 374	53.49 291	81.10 416	45.75 436	82.13 272	85.70 384

TinyColmap		67.30 393	64.81 400	74.76 378	81.92 383	56.68 378	80.29 366	81.49 375	60.33 397	56.27 453	83.22 369	24.77 458	87.66 358	45.52 437	69.47 411	79.95 442

pmmvs3		57.79 420	54.26 425	68.37 427	64.02 469	56.72 376	75.12 423	65.17 460	40.20 461	52.93 457	69.86 457	20.36 465	75.48 448	45.45 438	55.25 454	72.90 455

OpenMVS_ROB	64.09 19	70.56 364	68.19 370	77.65 344	80.26 404	59.41 341	85.01 275	82.96 358	58.76 414	65.43 411	82.33 385	37.63 431	91.23 288	45.34 439	76.03 352	82.32 427

test0.0.03 1		68.00 389	67.69 382	68.90 423	77.55 428	47.43 446	75.70 417	72.95 442	66.66 323	66.56 400	82.29 387	48.06 354	75.87 445	44.97 440	74.51 377	83.41 415

testgi		66.67 398	66.53 394	67.08 433	75.62 439	41.69 468	75.93 413	76.50 425	66.11 332	65.20 415	86.59 290	35.72 438	74.71 452	43.71 441	73.38 389	84.84 399

Anonymous20231206		68.60 382	67.80 380	71.02 414	80.23 406	50.75 437	78.30 397	80.47 387	56.79 429	66.11 408	82.63 382	46.35 370	78.95 424	43.62 442	75.70 355	83.36 416

FE-MVSNET		67.25 394	65.33 398	73.02 397	75.86 436	52.54 421	80.26 368	80.56 385	63.80 366	60.39 437	79.70 417	41.41 409	84.66 391	43.34 443	62.62 436	81.86 431

tfpnnormal		74.39 315	73.16 321	78.08 333	86.10 283	58.05 352	84.65 285	87.53 277	70.32 255	71.22 349	85.63 314	54.97 272	89.86 316	43.03 444	75.02 372	86.32 370

MIMVSNet1		68.58 383	66.78 393	73.98 387	80.07 408	51.82 427	80.77 355	84.37 329	64.40 354	59.75 442	82.16 389	36.47 435	83.63 397	42.73 445	70.33 408	86.48 369

ttmdpeth		59.91 418	57.10 422	68.34 428	67.13 465	46.65 452	74.64 426	67.41 455	48.30 451	62.52 432	85.04 332	20.40 464	75.93 444	42.55 446	45.90 466	82.44 426

test20.03		67.45 391	66.95 392	68.94 422	75.48 440	44.84 459	77.50 404	77.67 414	66.66 323	63.01 428	83.80 356	47.02 360	78.40 426	42.53 447	68.86 416	83.58 414

ADS-MVSNet2		66.20 404	63.33 408	74.82 377	79.92 409	58.75 344	67.55 453	75.19 430	53.37 440	65.25 413	75.86 442	42.32 402	80.53 419	41.57 448	68.91 414	85.18 392

ADS-MVSNet		64.36 409	62.88 412	68.78 425	79.92 409	47.17 449	67.55 453	71.18 444	53.37 440	65.25 413	75.86 442	42.32 402	73.99 456	41.57 448	68.91 414	85.18 392

Patchmatch-test		64.82 408	63.24 409	69.57 419	79.42 419	49.82 441	63.49 467	69.05 451	51.98 445	59.95 441	80.13 411	50.91 322	70.98 460	40.66 450	73.57 385	87.90 332

MVS-HIRNet		59.14 419	57.67 421	63.57 438	81.65 385	43.50 462	71.73 435	65.06 461	39.59 463	51.43 458	57.73 466	38.34 427	82.58 406	39.53 451	73.95 381	64.62 462

WAC-MVS						42.58 464								39.46 452

myMVS_eth3d		67.02 395	66.29 395	69.21 421	84.68 318	42.58 464	78.62 390	73.08 440	66.65 326	66.74 398	79.46 418	31.53 447	82.30 407	39.43 453	76.38 348	82.75 424

DSMNet-mixed		57.77 421	56.90 423	60.38 442	67.70 463	35.61 473	69.18 447	53.97 474	32.30 472	57.49 449	79.88 414	40.39 416	68.57 466	38.78 454	72.37 394	76.97 448

N_pmnet		52.79 429	53.26 427	51.40 454	78.99 422	7.68 488	69.52 445	3.89 487	51.63 446	57.01 450	74.98 446	40.83 413	65.96 469	37.78 455	64.67 430	80.56 441

testing3		68.56 384	67.67 383	71.22 413	87.33 241	42.87 463	83.06 330	71.54 443	70.36 252	69.08 373	84.38 342	30.33 450	85.69 378	37.50 456	75.45 363	85.09 396

MVStest1		56.63 422	52.76 428	68.25 429	61.67 471	53.25 419	71.67 436	68.90 453	38.59 464	50.59 460	83.05 373	25.08 456	70.66 461	36.76 457	38.56 467	80.83 438

test_0402		72.79 342	70.44 353	79.84 296	88.13 198	65.99 201	85.93 247	84.29 332	65.57 340	67.40 390	85.49 318	46.92 361	92.61 222	35.88 458	74.38 378	80.94 437

new_pmnet		50.91 432	50.29 432	52.78 453	68.58 462	34.94 475	63.71 465	56.63 473	39.73 462	44.95 464	65.47 459	21.93 463	58.48 473	34.98 459	56.62 448	64.92 461

APD_test1		53.31 428	49.93 433	63.42 439	65.68 466	50.13 439	71.59 437	66.90 457	34.43 469	40.58 468	71.56 454	8.65 479	76.27 440	34.64 460	55.36 452	63.86 463

Syy-MVS		68.05 388	67.85 377	68.67 426	84.68 318	40.97 469	78.62 390	73.08 440	66.65 326	66.74 398	79.46 418	52.11 305	82.30 407	32.89 461	76.38 348	82.75 424

dmvs_testset		62.63 413	64.11 404	58.19 444	78.55 424	24.76 482	75.28 419	65.94 459	67.91 310	60.34 438	76.01 441	53.56 289	73.94 457	31.79 462	67.65 420	75.88 451

UWE-MVS-28		65.32 405	64.93 399	66.49 434	78.70 423	38.55 471	77.86 403	64.39 463	62.00 387	64.13 421	83.60 363	41.44 408	76.00 443	31.39 463	80.89 285	84.92 397

ANet_high		50.57 433	46.10 437	63.99 437	48.67 482	39.13 470	70.99 440	80.85 380	61.39 391	31.18 471	57.70 467	17.02 469	73.65 458	31.22 464	15.89 479	79.18 444

EGC-MVSNET		52.07 431	47.05 435	67.14 432	83.51 346	60.71 323	80.50 362	67.75 454	0.07 482	0.43 483	75.85 444	24.26 459	81.54 412	28.82 465	62.25 437	59.16 465

PMMVS2		40.82 440	38.86 444	46.69 455	53.84 477	16.45 486	48.61 473	49.92 475	37.49 465	31.67 470	60.97 463	8.14 480	56.42 475	28.42 466	30.72 472	67.19 460

tmp_tt		18.61 447	21.40 450	10.23 464	4.82 487	10.11 487	34.70 475	30.74 485	1.48 481	23.91 477	26.07 478	28.42 452	13.41 483	27.12 467	15.35 480	7.17 478

test_method		31.52 443	29.28 447	38.23 458	27.03 486	6.50 489	20.94 478	62.21 466	4.05 480	22.35 478	52.50 471	13.33 471	47.58 478	27.04 468	34.04 470	60.62 464

testf1		45.72 435	41.96 439	57.00 445	56.90 473	45.32 454	66.14 458	59.26 470	26.19 473	30.89 472	60.96 464	4.14 482	70.64 462	26.39 469	46.73 464	55.04 468

APD_test2		45.72 435	41.96 439	57.00 445	56.90 473	45.32 454	66.14 458	59.26 470	26.19 473	30.89 472	60.96 464	4.14 482	70.64 462	26.39 469	46.73 464	55.04 468

FPMVS		53.68 427	51.64 429	59.81 443	65.08 467	51.03 434	69.48 446	69.58 449	41.46 460	40.67 467	72.32 452	16.46 470	70.00 464	24.24 471	65.42 428	58.40 467

Gipuma		45.18 438	41.86 441	55.16 451	77.03 433	51.52 430	32.50 476	80.52 386	32.46 471	27.12 474	35.02 475	9.52 477	75.50 447	22.31 472	60.21 444	38.45 474
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		45.42 437	45.38 438	45.55 456	73.36 452	26.85 480	67.72 452	34.19 482	54.15 438	49.65 462	56.41 469	25.43 455	62.94 472	19.45 473	28.09 473	46.86 472

DeepMVS_CX				27.40 462	40.17 485	26.90 479		24.59 486	17.44 478	23.95 476	48.61 473	9.77 476	26.48 481	18.06 474	24.47 475	28.83 475

WB-MVS		54.94 423	54.72 424	55.60 450	73.50 449	20.90 484	74.27 429	61.19 467	59.16 409	50.61 459	74.15 447	47.19 359	75.78 446	17.31 475	35.07 469	70.12 457

PMVS	37.38 22	44.16 439	40.28 443	55.82 449	40.82 484	42.54 466	65.12 462	63.99 464	34.43 469	24.48 475	57.12 468	3.92 484	76.17 442	17.10 476	55.52 451	48.75 470
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 445	25.89 449	43.81 457	44.55 483	35.46 474	28.87 477	39.07 481	18.20 477	18.58 479	40.18 474	2.68 485	47.37 479	17.07 477	23.78 476	48.60 471
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
SSC-MVS		53.88 426	53.59 426	54.75 452	72.87 455	19.59 485	73.84 431	60.53 469	57.58 425	49.18 463	73.45 450	46.34 371	75.47 449	16.20 478	32.28 471	69.20 458

E-PMN		31.77 442	30.64 445	35.15 460	52.87 480	27.67 477	57.09 471	47.86 478	24.64 475	16.40 480	33.05 476	11.23 475	54.90 476	14.46 479	18.15 477	22.87 476

EMVS		30.81 444	29.65 446	34.27 461	50.96 481	25.95 481	56.58 472	46.80 479	24.01 476	15.53 481	30.68 477	12.47 472	54.43 477	12.81 480	17.05 478	22.43 477

kuosan		39.70 441	40.40 442	37.58 459	64.52 468	26.98 478	65.62 460	33.02 483	46.12 454	42.79 466	48.99 472	24.10 460	46.56 480	12.16 481	26.30 474	39.20 473

wuyk23d		16.82 448	15.94 451	19.46 463	58.74 472	31.45 476	39.22 474	3.74 488	6.84 479	6.04 482	2.70 482	1.27 486	24.29 482	10.54 482	14.40 481	2.63 479

testmvs		6.04 451	8.02 454	0.10 466	0.08 488	0.03 491	69.74 444	0.04 489	0.05 483	0.31 484	1.68 483	0.02 488	0.04 484	0.24 483	0.02 482	0.25 481

test123		6.12 450	8.11 453	0.14 465	0.06 489	0.09 490	71.05 439	0.03 490	0.04 484	0.25 485	1.30 484	0.05 487	0.03 485	0.21 484	0.01 483	0.29 480

mmdepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

monomultidepth		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

test_blank		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet_test		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

DCPMVS		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

cdsmvs_eth3d_5k		19.96 446	26.61 448	0.00 467	0.00 490	0.00 492	0.00 479	89.26 218	0.00 485	0.00 486	88.61 229	61.62 203	0.00 486	0.00 485	0.00 484	0.00 482

pcd_1.5k_mvsjas		5.26 452	7.02 455	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	63.15 175	0.00 486	0.00 485	0.00 484	0.00 482

sosnet-low-res		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

sosnet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uncertanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

Regformer		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

ab-mvs-re		7.23 449	9.64 452	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	86.72 282	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

uanet		0.00 453	0.00 456	0.00 467	0.00 490	0.00 492	0.00 479	0.00 491	0.00 485	0.00 486	0.00 485	0.00 489	0.00 486	0.00 485	0.00 484	0.00 482

TestfortrainingZip							93.28 12

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 148	91.30 18

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

eth-test2					0.00 490

eth-test					0.00 490

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

save fliter					93.80 44	72.35 44	90.47 74	91.17 144	74.31 153

test0726					95.27 5	71.25 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

GSMVS																88.96 303

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 318				88.96 303

sam_mvs												50.01 334

MTGPA								92.02 106

test_post											5.46 480	50.36 330	84.24 392

patchmatchnet-post											74.00 448	51.12 321	88.60 344

MTMP							92.18 39	32.83 484

TEST9					93.26 56	72.96 25	88.75 138	91.89 114	68.44 304	85.00 80	93.10 88	74.36 32	95.41 80

test_8					93.13 60	72.57 35	88.68 143	91.84 118	68.69 298	84.87 84	93.10 88	74.43 30	95.16 90

agg_prior					92.85 68	71.94 52		91.78 122		84.41 95			94.93 101

test_prior4						72.60 34	89.01 125

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 81

新几何2							86.29 238

旧先验1					91.96 80	65.79 208		86.37 304			93.08 92	69.31 98			92.74 80	88.74 314

原ACMM2							86.86 212

test222					91.50 86	68.26 137	84.16 302	83.20 352	54.63 437	79.74 179	91.63 130	58.97 239			91.42 103	86.77 363

segment_acmp												73.08 43

testdata1							84.14 303		75.71 107

test12				86.80 58	92.63 73	70.70 81		91.79 121		82.71 132		71.67 63	96.16 52		94.50 57	93.54 110

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 229

plane_prior4											91.00 158

plane_prior3						68.60 128			78.44 36	78.92 194

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 202

n2								0.00 491

nn								0.00 491

door-mid								69.98 447

test11								92.23 92

door								69.44 450

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 86	77.23 235

ACMP_Plane					89.33 144		89.17 116		76.41 86	77.23 235

HQP4-MVS										77.24 234			95.11 94			91.03 216

HQP3-MVS								92.19 100							85.99 206

HQP2-MVS												60.17 232

NP-MVS					89.62 129	68.32 135					90.24 179

ACMMP++_ref															81.95 275

ACMMP++															81.25 280

Test By Simon												64.33 161

High-resolution multi-view benchmark