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.

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 113	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 56
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 146	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
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

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	93.33 122
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_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 37

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_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

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 68

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 106	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 84
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 147	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 132

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 137	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 139

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 137	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 139

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	93.70 100

fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 233	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

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

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 133	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 111	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

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 99	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 76

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 125	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

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 97	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 125	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 53

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 97	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 206	87.08 259	65.21 224	89.09 123	90.21 182	79.67 19	89.98 24	95.02 24	73.17 42	91.71 268	91.30 3	91.60 99	92.34 172

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 148	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_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 101	79.94 17	89.74 27	94.86 26	68.63 113	94.20 136	90.83 5	91.39 104	94.38 60

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 227	92.02 110	79.45 22	85.88 70	94.80 27	68.07 121	96.21 50	86.69 52	95.34 36	93.23 125

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.
fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 245	67.30 174	89.50 101	90.98 153	76.25 100	90.56 22	94.75 29	68.38 116	94.24 135	90.80 7	92.32 89	94.19 70

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 168	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 151	86.84 64	94.65 31	67.31 130	95.77 64	84.80 68	92.85 78	92.84 153

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	69.09 104	96.70 31	84.37 74	94.83 49	94.03 79

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	68.81 110	96.65 34	84.53 72	94.90 45	94.00 81

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 197	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 155
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 179	85.94 69	94.51 35	65.80 153	95.61 67	83.04 89	92.51 83	93.53 115

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	67.93 123	95.95 62	84.20 78	94.39 61	93.23 125

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 92	73.53 180	85.69 73	94.45 37	65.00 161	95.56 68	82.75 94	91.87 95	92.50 165

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 92	73.53 180	85.69 73	94.45 37	63.87 169		82.75 94	91.87 95	92.50 165

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	93.66 101

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 116	83.86 108	94.42 40	67.87 125	96.64 35	82.70 98	94.57 56	93.66 101

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	93.62 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 250	68.54 130	89.57 99	90.44 171	75.31 124	87.49 54	94.39 42	72.86 47	92.72 224	89.04 27	90.56 118	94.16 71

fmvsm_s_conf0.1_n_2		83.80 105	83.79 105	83.83 178	85.62 296	64.94 237	87.03 206	86.62 308	74.32 156	87.97 47	94.33 43	60.67 227	92.60 227	89.72 14	87.79 174	93.96 82

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 189	87.12 258	66.01 199	88.56 148	89.43 209	75.59 115	89.32 28	94.32 44	72.89 46	91.21 295	90.11 11	92.33 87	93.16 132

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 65

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 205	82.14 3	86.65 66	94.28 46	68.28 119	97.46 6	90.81 6	95.31 38	95.15 8

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 410	69.03 110	89.47 102	89.65 201	73.24 191	86.98 62	94.27 47	66.62 137	93.23 194	90.26 10	89.95 130	93.78 97

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	93.05 141

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 99	76.87 74	82.81 135	94.25 49	66.44 141	96.24 49	82.88 92	94.28 64	93.38 118

fmvsm_s_conf0.5_n_2		84.04 98	84.11 98	83.81 180	86.17 283	65.00 232	86.96 209	87.28 287	74.35 155	88.25 39	94.23 50	61.82 203	92.60 227	89.85 12	88.09 167	93.84 91

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	82.82 134	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 105
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 171	87.78 218	66.09 196	89.96 86	90.80 161	77.37 57	86.72 65	94.20 52	72.51 51	92.78 223	89.08 22	92.33 87	93.13 136

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	67.45 128	96.60 37	83.06 87	94.50 57	94.07 77

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 37
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
test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 368	69.39 107	89.65 95	90.29 180	73.31 187	87.77 49	94.15 55	71.72 61	93.23 194	90.31 9	90.67 117	93.89 88

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 46

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 133	73.89 169	82.67 137	94.09 57	62.60 187	95.54 70	80.93 111	92.93 77	93.57 111

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 238	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

fmvsm_s_conf0.1_n_a		83.32 125	82.99 122	84.28 145	83.79 341	68.07 145	89.34 111	82.85 368	69.80 272	87.36 58	94.06 59	68.34 118	91.56 274	87.95 42	83.46 259	93.21 128

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 63

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 315	69.51 100	89.62 98	90.58 166	73.42 183	87.75 50	94.02 61	72.85 48	93.24 193	90.37 8	90.75 115	93.96 82

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

PC_three_1452									68.21 311	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

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	93.65 105
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
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 87

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 285	69.93 92	88.65 144	90.78 162	69.97 268	88.27 38	93.98 66	71.39 67	91.54 278	88.49 35	90.45 120	93.91 85

fmvsm_s_conf0.1_n		83.56 116	83.38 115	84.10 154	84.86 317	67.28 175	89.40 108	83.01 363	70.67 245	87.08 60	93.96 67	68.38 116	91.45 284	88.56 34	84.50 233	93.56 112

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 103	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 65
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
fmvsm_s_conf0.5_n_7		83.34 123	84.03 99	81.28 265	85.73 293	65.13 227	85.40 268	89.90 192	74.96 139	82.13 143	93.89 69	66.65 136	87.92 361	86.56 53	91.05 109	90.80 228

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 150	86.26 279	67.40 170	89.18 115	89.31 218	72.50 202	88.31 37	93.86 70	69.66 93	91.96 256	89.81 13	91.05 109	93.38 118

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 150	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		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 171	93.82 72	64.33 165	96.29 46	82.67 99	90.69 116	93.23 125
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
fmvsm_s_conf0.5_n_a		83.63 114	83.41 114	84.28 145	86.14 284	68.12 143	89.43 104	82.87 367	70.27 261	87.27 59	93.80 73	69.09 104	91.58 271	88.21 38	83.65 253	93.14 135

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 143	86.70 270	65.83 205	88.77 136	89.78 194	75.46 119	88.35 36	93.73 74	69.19 103	93.06 209	91.30 3	88.44 159	94.02 80

fmvsm_s_conf0.5_n		83.80 105	83.71 107	84.07 160	86.69 271	67.31 173	89.46 103	83.07 362	71.09 233	86.96 63	93.70 75	69.02 109	91.47 283	88.79 30	84.62 232	93.44 117

test_prior2							88.85 132		75.41 120	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 147	85.42 302	68.81 116	88.49 150	87.26 291	68.08 312	88.03 44	93.49 77	72.04 57	91.77 264	88.90 29	89.14 146	92.24 179

VDDNet		81.52 163	80.67 163	84.05 166	90.44 108	64.13 259	89.73 93	85.91 319	71.11 232	83.18 125	93.48 78	50.54 336	93.49 178	73.40 209	88.25 164	94.54 52

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 310	84.61 91	93.48 78	72.32 52	96.15 53	79.00 139	95.43 34	94.28 67

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 75

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 184	87.32 247	65.13 227	88.86 130	91.63 132	75.41 120	88.23 40	93.45 81	68.56 114	92.47 235	89.52 18	92.78 79	93.20 130

fmvsm_l_conf0.5_n_a		84.13 96	84.16 94	84.06 163	85.38 303	68.40 133	88.34 158	86.85 302	67.48 319	87.48 55	93.40 82	70.89 73	91.61 269	88.38 37	89.22 143	92.16 186

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	76.66 253	93.37 83	60.40 235	96.75 30	77.20 161	93.73 70	95.29 6

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 71
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 133	82.36 135	84.96 107	91.02 95	66.40 191	88.91 128	88.11 262	77.57 49	84.39 96	93.29 85	52.19 307	93.91 152	77.05 164	88.70 154	94.57 48

test_fmvsmvis_n_1920		84.02 99	83.87 101	84.49 130	84.12 333	69.37 108	88.15 166	87.96 269	70.01 266	83.95 107	93.23 86	68.80 111	91.51 281	88.61 32	89.96 129	92.57 160

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 159	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 233	90.88 114	93.07 138

TEST9					93.26 56	72.96 25	88.75 138	91.89 118	68.44 308	85.00 80	93.10 88	74.36 32	95.41 80

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 118	68.69 303	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 143

test_8					93.13 60	72.57 35	88.68 143	91.84 122	68.69 303	84.87 84	93.10 88	74.43 30	95.16 90

LFMVS		81.82 153	81.23 153	83.57 187	91.89 82	63.43 282	89.84 87	81.85 379	77.04 70	83.21 122	93.10 88	52.26 306	93.43 185	71.98 228	89.95 130	93.85 89

旧先验1					91.96 80	65.79 208		86.37 312			93.08 92	69.31 99			92.74 80	88.74 318

dcpmvs_2		85.63 70	86.15 60	84.06 163	91.71 84	64.94 237	86.47 231	91.87 120	73.63 175	86.60 67	93.02 93	76.57 18	91.87 262	83.36 84	92.15 90	95.35 3

testdata				79.97 300	90.90 98	64.21 257		84.71 333	59.27 416	85.40 75	92.91 94	62.02 200	89.08 342	68.95 261	91.37 105	86.63 376

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 195	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 59

Vis-MVSNet		83.46 119	82.80 126	85.43 90	90.25 112	68.74 121	90.30 80	90.13 185	76.33 96	80.87 168	92.89 95	61.00 222	94.20 136	72.45 225	90.97 111	93.35 121
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 109	83.33 117	84.92 111	93.28 53	70.86 78	92.09 41	90.38 173	68.75 302	79.57 186	92.83 97	60.60 231	93.04 212	80.92 112	91.56 102	90.86 227

3Dnovator	76.31 5	83.38 122	82.31 136	86.59 61	87.94 208	72.94 28	90.64 68	92.14 109	77.21 63	75.47 279	92.83 97	58.56 247	94.72 115	73.24 212	92.71 81	92.13 187

MSLP-MVS++		85.43 75	85.76 69	84.45 131	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 132	92.94 214	80.36 119	94.35 63	90.16 257

test2506		77.30 277	76.49 274	79.74 307	90.08 116	52.02 430	87.86 178	63.10 473	74.88 142	80.16 180	92.79 100	38.29 436	92.35 242	68.74 264	92.50 84	94.86 19

ECVR-MVS		79.61 211	79.26 204	80.67 282	90.08 116	54.69 412	87.89 176	77.44 426	74.88 142	80.27 177	92.79 100	48.96 359	92.45 236	68.55 265	92.50 84	94.86 19

test1111		79.43 218	79.18 207	80.15 296	89.99 121	53.31 425	87.33 198	77.05 430	75.04 135	80.23 179	92.77 102	48.97 358	92.33 244	68.87 262	92.40 86	94.81 22

MG-MVS		83.41 120	83.45 113	83.28 196	92.74 71	62.28 307	88.17 164	89.50 207	75.22 127	81.49 155	92.74 103	66.75 135	95.11 94	72.85 215	91.58 101	92.45 169

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
patch_mono-2		83.65 112	84.54 89	80.99 274	90.06 120	65.83 205	84.21 303	88.74 251	71.60 221	85.01 79	92.44 105	74.51 29	83.50 407	82.15 101	92.15 90	93.64 107

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 250	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
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

E6new		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 90	76.51 85	83.53 116	92.26 108	69.26 100	93.49 178	79.88 125	88.26 161	94.69 33

E6		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 90	76.51 85	83.53 116	92.26 108	69.26 100	93.49 178	79.88 125	88.26 161	94.69 33

E5		84.22 92	84.12 95	84.51 127	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 102	93.50 177	79.88 125	88.26 161	94.69 33

baseline		84.93 86	84.98 83	84.80 117	87.30 248	65.39 218	87.30 199	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

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 104	79.31 24	84.39 96	92.18 112	64.64 163	95.53 71	80.70 116	94.65 52	94.56 50

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 266	79.31 24	84.39 96	92.18 112	64.64 163	95.53 71	80.70 116	90.91 113	93.21 128

QAPM		80.88 174	79.50 197	85.03 104	88.01 206	68.97 114	91.59 51	92.00 112	66.63 332	75.15 297	92.16 114	57.70 254	95.45 75	63.52 304	88.76 152	90.66 236

IS-MVSNet		83.15 128	82.81 125	84.18 152	89.94 123	63.30 284	91.59 51	88.46 259	79.04 30	79.49 187	92.16 114	65.10 158	94.28 130	67.71 271	91.86 97	94.95 12

viewmacassd2359aftdt		83.76 108	83.66 109	84.07 160	86.59 274	64.56 246	86.88 214	91.82 123	75.72 110	83.34 121	92.15 116	68.24 120	92.88 217	79.05 135	89.15 145	94.77 25

BP-MVS1		84.32 91	83.71 107	86.17 68	87.84 213	67.85 154	89.38 109	89.64 202	77.73 45	83.98 106	92.12 117	56.89 265	95.43 77	84.03 80	91.75 98	95.24 7

E4		84.10 97	83.99 100	84.45 131	87.58 238	64.99 233	86.54 229	92.25 95	76.38 93	83.37 120	92.09 118	69.88 90	93.58 166	79.78 129	88.03 170	94.77 25

新几何1				83.42 191	93.13 60	70.71 80		85.48 325	57.43 434	81.80 149	91.98 119	63.28 173	92.27 245	64.60 299	92.99 76	87.27 356

OpenMVS	72.83 10	79.77 209	78.33 225	84.09 158	85.17 308	69.91 93	90.57 69	90.97 154	66.70 326	72.17 343	91.91 120	54.70 283	93.96 144	61.81 330	90.95 112	88.41 327

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 203	85.22 78	91.90 121	69.47 95	96.42 44	83.28 86	95.94 23	94.35 62

VNet		82.21 144	82.41 133	81.62 254	90.82 100	60.93 327	84.47 292	89.78 194	76.36 95	84.07 104	91.88 122	64.71 162	90.26 318	70.68 240	88.89 148	93.66 101

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	91.88 122	69.04 108	95.43 77	83.93 81	93.77 69	93.01 144

GDP-MVS		83.52 117	82.64 129	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 201	83.71 111	91.86 124	55.69 273	95.35 86	80.03 122	89.74 134	94.69 33

KinetiMVS		83.31 126	82.61 130	85.39 91	87.08 259	67.56 165	88.06 168	91.65 131	77.80 44	82.21 142	91.79 125	57.27 260	94.07 142	77.77 154	89.89 132	94.56 50

E2		84.00 100	83.87 101	84.39 134	87.70 226	64.95 234	86.40 236	92.23 96	75.85 107	83.21 122	91.78 126	70.09 85	93.55 171	79.52 132	88.05 168	94.66 40

E3		84.00 100	83.87 101	84.39 134	87.70 226	64.95 234	86.40 236	92.23 96	75.85 107	83.21 122	91.78 126	70.09 85	93.55 171	79.52 132	88.05 168	94.66 40

OPM-MVS		83.50 118	82.95 123	85.14 98	88.79 172	70.95 74	89.13 121	91.52 137	77.55 52	80.96 165	91.75 128	60.71 225	94.50 124	79.67 131	86.51 199	89.97 273
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 200	84.64 90	91.71 129	71.85 58	96.03 55	84.77 69	94.45 60	94.49 55

viewmanbaseed2359cas		83.66 111	83.55 111	84.00 171	86.81 266	64.53 247	86.65 224	91.75 128	74.89 141	83.15 127	91.68 130	68.74 112	92.83 221	79.02 137	89.24 142	94.63 43

XVG-OURS-SEG-HR		80.81 177	79.76 188	83.96 175	85.60 297	68.78 118	83.54 322	90.50 169	70.66 248	76.71 252	91.66 131	60.69 226	91.26 290	76.94 165	81.58 282	91.83 192

EPNet		83.72 110	82.92 124	86.14 72	84.22 331	69.48 101	91.05 64	85.27 326	81.30 6	76.83 248	91.65 132	66.09 148	95.56 68	76.00 180	93.85 68	93.38 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 137	81.97 146	84.85 114	88.75 174	67.42 168	87.98 170	90.87 158	74.92 140	79.72 184	91.65 132	62.19 197	93.96 144	75.26 191	86.42 200	93.16 132

viewdifsd2359ckpt07		82.83 136	82.78 128	82.99 213	86.51 276	62.58 298	85.09 276	90.83 160	75.22 127	82.28 139	91.63 134	69.43 96	92.03 252	77.71 155	86.32 201	94.34 63

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	91.63 134	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

test222					91.50 86	68.26 137	84.16 306	83.20 360	54.63 445	79.74 183	91.63 134	58.97 243			91.42 103	86.77 371

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 249	90.33 177	76.11 102	82.08 144	91.61 137	71.36 68	94.17 139	81.02 110	92.58 82	92.08 188

原ACMM1				84.35 138	93.01 66	68.79 117		92.44 82	63.96 372	81.09 162	91.57 138	66.06 149	95.45 75	67.19 278	94.82 50	88.81 313

viewcassd2359sk11		83.89 102	83.74 106	84.34 139	87.76 221	64.91 240	86.30 240	92.22 99	75.47 118	83.04 128	91.52 139	70.15 83	93.53 174	79.26 134	87.96 171	94.57 48

LPG-MVS_test		82.08 146	81.27 152	84.50 128	89.23 152	68.76 119	90.22 81	91.94 116	75.37 122	76.64 254	91.51 140	54.29 286	94.91 102	78.44 145	83.78 246	89.83 278

LGP-MVS_train				84.50 128	89.23 152	68.76 119		91.94 116	75.37 122	76.64 254	91.51 140	54.29 286	94.91 102	78.44 145	83.78 246	89.83 278

XVG-OURS		80.41 194	79.23 205	83.97 174	85.64 295	69.02 112	83.03 335	90.39 172	71.09 233	77.63 230	91.49 142	54.62 285	91.35 287	75.71 183	83.47 258	91.54 203

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 101	87.73 52	91.46 143	70.32 80	93.78 158	81.51 104	88.95 147	94.63 43

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	81.32 157	91.43 144	70.34 79	97.23 17	84.26 75	93.36 74	94.37 61

h-mvs33		83.15 128	82.19 139	86.02 76	90.56 105	70.85 79	88.15 166	89.16 228	76.02 104	84.67 87	91.39 145	61.54 208	95.50 73	82.71 96	75.48 365	91.72 199

MGCFI-Net		85.06 85	85.51 74	83.70 182	89.42 139	63.01 290	89.43 104	92.62 78	76.43 88	87.53 53	91.34 146	72.82 49	93.42 186	81.28 108	88.74 153	94.66 40

nrg030		83.88 103	83.53 112	84.96 107	86.77 268	69.28 109	90.46 75	92.67 72	74.79 145	82.95 129	91.33 147	72.70 50	93.09 207	80.79 115	79.28 313	92.50 165

E3new		83.78 107	83.60 110	84.31 141	87.76 221	64.89 241	86.24 243	92.20 102	75.15 134	82.87 131	91.23 148	70.11 84	93.52 176	79.05 135	87.79 174	94.51 54

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	91.23 148	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	91.23 148	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 203	93.04 46	69.80 272	82.85 133	91.22 151	73.06 44	96.02 57	76.72 173	94.63 54	91.46 209

Anonymous202405211		78.25 249	77.01 260	81.99 248	91.03 94	60.67 333	84.77 283	83.90 346	70.65 249	80.00 181	91.20 152	41.08 420	91.43 285	65.21 293	85.26 224	93.85 89

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	83.51 119	91.20 152	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

Anonymous20240529		80.19 204	78.89 213	84.10 154	90.60 104	64.75 244	88.95 127	90.90 156	65.97 340	80.59 173	91.17 154	49.97 343	93.73 164	69.16 259	82.70 271	93.81 93

EPP-MVSNet		83.40 121	83.02 121	84.57 123	90.13 114	64.47 252	92.32 35	90.73 163	74.45 154	79.35 192	91.10 155	69.05 107	95.12 92	72.78 216	87.22 185	94.13 73

TAPA-MVS	73.13 9	79.15 227	77.94 233	82.79 227	89.59 130	62.99 294	88.16 165	91.51 138	65.77 341	77.14 245	91.09 156	60.91 223	93.21 196	50.26 418	87.05 189	92.17 185
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 166	83.16 126	91.07 157	75.94 21	95.19 89	79.94 124	94.38 62	93.55 113

FIs		82.07 147	82.42 132	81.04 273	88.80 171	58.34 357	88.26 161	93.49 31	76.93 72	78.47 210	91.04 158	69.92 89	92.34 243	69.87 252	84.97 226	92.44 170

MVS_111021_LR		82.61 139	82.11 140	84.11 153	88.82 166	71.58 57	85.15 273	86.16 316	74.69 147	80.47 176	91.04 158	62.29 194	90.55 315	80.33 120	90.08 127	90.20 256

DP-MVS Recon		83.11 131	82.09 142	86.15 70	94.44 23	70.92 76	88.79 135	92.20 102	70.53 250	79.17 194	91.03 160	64.12 167	96.03 55	68.39 268	90.14 125	91.50 205

mamv4		76.81 285	78.23 229	72.54 410	86.12 285	65.75 210	78.76 396	82.07 376	64.12 366	72.97 331	91.02 161	67.97 122	68.08 475	83.04 89	78.02 327	83.80 420

HQP_MVS		83.64 113	83.14 118	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 198	91.00 162	60.42 233	95.38 82	78.71 143	86.32 201	91.33 210

plane_prior4											91.00 162

FC-MVSNet-test		81.52 163	82.02 144	80.03 298	88.42 187	55.97 397	87.95 172	93.42 34	77.10 68	77.38 234	90.98 164	69.96 88	91.79 263	68.46 267	84.50 233	92.33 173

diffmvs_AUTHOR		82.38 142	82.27 138	82.73 232	83.26 355	63.80 266	83.89 310	89.76 196	73.35 186	82.37 138	90.84 165	66.25 144	90.79 309	82.77 93	87.93 172	93.59 110

Vis-MVSNet (Re-imp)		78.36 248	78.45 220	78.07 343	88.64 178	51.78 436	86.70 222	79.63 408	74.14 163	75.11 298	90.83 166	61.29 216	89.75 328	58.10 366	91.60 99	92.69 157

114514_t		80.68 185	79.51 196	84.20 151	94.09 42	67.27 176	89.64 96	91.11 151	58.75 423	74.08 316	90.72 167	58.10 250	95.04 99	69.70 253	89.42 140	90.30 253

viewdifsd2359ckpt13		82.91 134	82.29 137	84.77 118	86.96 262	66.90 187	87.47 187	91.62 133	72.19 208	81.68 152	90.71 168	66.92 134	93.28 189	75.90 181	87.15 187	94.12 74

viewdifsd2359ckpt09		83.34 123	82.55 131	85.70 81	87.64 230	67.72 159	88.43 151	91.68 130	71.91 215	81.65 153	90.68 169	67.10 133	94.75 113	76.17 176	87.70 177	94.62 45

PAPM_NR		83.02 132	82.41 133	84.82 115	92.47 76	66.37 192	87.93 174	91.80 124	73.82 170	77.32 236	90.66 170	67.90 124	94.90 104	70.37 243	89.48 139	93.19 131

viewdifsd2359ckpt11		80.37 198	79.73 189	82.30 241	83.70 345	62.39 302	84.20 304	86.67 304	73.22 192	80.90 166	90.62 171	63.00 184	91.56 274	76.81 170	78.44 320	92.95 148

viewmsd2359difaftdt		80.37 198	79.73 189	82.30 241	83.70 345	62.39 302	84.20 304	86.67 304	73.22 192	80.90 166	90.62 171	63.00 184	91.56 274	76.81 170	78.44 320	92.95 148

LS3D		76.95 283	74.82 302	83.37 194	90.45 107	67.36 172	89.15 120	86.94 299	61.87 396	69.52 373	90.61 173	51.71 321	94.53 122	46.38 440	86.71 196	88.21 332

AstraMVS		80.81 177	80.14 178	82.80 224	86.05 288	63.96 261	86.46 232	85.90 320	73.71 173	80.85 169	90.56 174	54.06 290	91.57 273	79.72 130	83.97 244	92.86 151

VPNet		78.69 240	78.66 216	78.76 326	88.31 190	55.72 401	84.45 295	86.63 307	76.79 76	78.26 214	90.55 175	59.30 241	89.70 330	66.63 282	77.05 338	90.88 226

UniMVSNet_ETH3D		79.10 229	78.24 227	81.70 253	86.85 264	60.24 340	87.28 200	88.79 245	74.25 160	76.84 247	90.53 176	49.48 349	91.56 274	67.98 269	82.15 275	93.29 123

ACMP	74.13 6	81.51 165	80.57 165	84.36 137	89.42 139	68.69 126	89.97 85	91.50 141	74.46 153	75.04 301	90.41 177	53.82 292	94.54 121	77.56 157	82.91 266	89.86 277
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
SSM_0407		81.58 160	80.48 168	84.87 113	88.81 167	67.96 149	87.37 195	89.25 223	71.06 235	79.48 188	90.39 178	59.57 238	94.48 126	72.45 225	85.93 212	92.18 182

SSM_0404		81.91 150	80.84 161	85.13 101	89.24 151	68.26 137	87.84 179	89.25 223	71.06 235	80.62 172	90.39 178	59.57 238	94.65 119	72.45 225	87.19 186	92.47 168

viewmambaseed2359dif		80.41 194	79.84 186	82.12 243	82.95 370	62.50 301	83.39 323	88.06 266	67.11 321	80.98 164	90.31 180	66.20 146	91.01 303	74.62 195	84.90 227	92.86 151

RRT-MVS		82.60 141	82.10 141	84.10 154	87.98 207	62.94 295	87.45 190	91.27 144	77.42 56	79.85 182	90.28 181	56.62 268	94.70 117	79.87 128	88.15 166	94.67 37

PCF-MVS	73.52 7	80.38 196	78.84 214	85.01 105	87.71 224	68.99 113	83.65 316	91.46 142	63.00 380	77.77 228	90.28 181	66.10 147	95.09 98	61.40 333	88.22 165	90.94 225
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 129	68.32 135					90.24 183

HQP-MVS		82.61 139	82.02 144	84.37 136	89.33 144	66.98 183	89.17 116	92.19 104	76.41 89	77.23 239	90.23 184	60.17 236	95.11 94	77.47 158	85.99 210	91.03 220

PS-MVSNAJss		82.07 147	81.31 151	84.34 139	86.51 276	67.27 176	89.27 112	91.51 138	71.75 216	79.37 191	90.22 185	63.15 179	94.27 131	77.69 156	82.36 274	91.49 206

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 287	76.41 89	85.80 71	90.22 185	74.15 35	95.37 85	81.82 103	91.88 94	92.65 159

SDMVSNet		80.38 196	80.18 175	80.99 274	89.03 161	64.94 237	80.45 371	89.40 210	75.19 131	76.61 256	89.98 187	60.61 230	87.69 365	76.83 169	83.55 255	90.33 251

sd_testset		77.70 268	77.40 253	78.60 329	89.03 161	60.02 342	79.00 392	85.83 321	75.19 131	76.61 256	89.98 187	54.81 278	85.46 390	62.63 319	83.55 255	90.33 251

TranMVSNet+NR-MVSNet		80.84 175	80.31 172	82.42 238	87.85 212	62.33 305	87.74 181	91.33 143	80.55 9	77.99 222	89.86 189	65.23 157	92.62 225	67.05 280	75.24 375	92.30 175

diffmvs		82.10 145	81.88 147	82.76 230	83.00 365	63.78 268	83.68 315	89.76 196	72.94 198	82.02 145	89.85 190	65.96 152	90.79 309	82.38 100	87.30 184	93.71 99
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Elysia		81.53 161	80.16 176	85.62 84	85.51 299	68.25 139	88.84 133	92.19 104	71.31 226	80.50 174	89.83 191	46.89 370	94.82 108	76.85 166	89.57 136	93.80 95

StellarMVS		81.53 161	80.16 176	85.62 84	85.51 299	68.25 139	88.84 133	92.19 104	71.31 226	80.50 174	89.83 191	46.89 370	94.82 108	76.85 166	89.57 136	93.80 95

mamba_0408		79.37 223	77.52 250	84.93 110	88.81 167	67.96 149	65.03 471	88.66 253	70.96 239	79.48 188	89.80 193	58.69 244	94.65 119	70.35 244	85.93 212	92.18 182

SSM_04072		77.67 270	77.52 250	78.12 341	88.81 167	67.96 149	65.03 471	88.66 253	70.96 239	79.48 188	89.80 193	58.69 244	74.23 463	70.35 244	85.93 212	92.18 182

BH-RMVSNet		79.61 211	78.44 221	83.14 204	89.38 143	65.93 202	84.95 280	87.15 294	73.56 178	78.19 216	89.79 195	56.67 267	93.36 187	59.53 349	86.74 195	90.13 259

GeoE		81.71 155	81.01 158	83.80 181	89.51 134	64.45 253	88.97 126	88.73 252	71.27 229	78.63 204	89.76 196	66.32 143	93.20 199	69.89 251	86.02 209	93.74 98

guyue		81.13 170	80.64 164	82.60 235	86.52 275	63.92 264	86.69 223	87.73 277	73.97 165	80.83 170	89.69 197	56.70 266	91.33 289	78.26 152	85.40 223	92.54 162

AdaColmap		80.58 192	79.42 198	84.06 163	93.09 63	68.91 115	89.36 110	88.97 239	69.27 285	75.70 275	89.69 197	57.20 262	95.77 64	63.06 312	88.41 160	87.50 348

ACMM	73.20 8	80.78 184	79.84 186	83.58 186	89.31 147	68.37 134	89.99 84	91.60 135	70.28 260	77.25 237	89.66 199	53.37 297	93.53 174	74.24 201	82.85 267	88.85 311
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 255	76.79 267	81.97 249	90.40 109	71.07 70	87.59 184	84.55 336	66.03 339	72.38 340	89.64 200	57.56 256	86.04 382	59.61 348	83.35 260	88.79 314

test_yl		81.17 168	80.47 169	83.24 199	89.13 156	63.62 269	86.21 244	89.95 190	72.43 206	81.78 150	89.61 201	57.50 257	93.58 166	70.75 238	86.90 191	92.52 163

DCV-MVSNet		81.17 168	80.47 169	83.24 199	89.13 156	63.62 269	86.21 244	89.95 190	72.43 206	81.78 150	89.61 201	57.50 257	93.58 166	70.75 238	86.90 191	92.52 163

EI-MVSNet-Vis-set		84.19 95	83.81 104	85.31 93	88.18 194	67.85 154	87.66 182	89.73 199	80.05 15	82.95 129	89.59 203	70.74 76	94.82 108	80.66 118	84.72 230	93.28 124

PAPR		81.66 158	80.89 160	83.99 173	90.27 111	64.00 260	86.76 221	91.77 127	68.84 301	77.13 246	89.50 204	67.63 126	94.88 106	67.55 273	88.52 157	93.09 137

jajsoiax		79.29 224	77.96 232	83.27 197	84.68 322	66.57 190	89.25 113	90.16 184	69.20 290	75.46 281	89.49 205	45.75 387	93.13 205	76.84 168	80.80 292	90.11 261

MVSFormer		82.85 135	82.05 143	85.24 95	87.35 240	70.21 86	90.50 72	90.38 173	68.55 305	81.32 157	89.47 206	61.68 205	93.46 183	78.98 140	90.26 123	92.05 189

jason		81.39 166	80.29 173	84.70 121	86.63 273	69.90 94	85.95 250	86.77 303	63.24 376	81.07 163	89.47 206	61.08 221	92.15 249	78.33 148	90.07 128	92.05 189
jason: jason.
mvs_tets		79.13 228	77.77 242	83.22 201	84.70 321	66.37 192	89.17 116	90.19 183	69.38 282	75.40 284	89.46 208	44.17 399	93.15 203	76.78 172	80.70 294	90.14 258

UGNet		80.83 176	79.59 195	84.54 124	88.04 203	68.09 144	89.42 106	88.16 261	76.95 71	76.22 265	89.46 208	49.30 353	93.94 147	68.48 266	90.31 121	91.60 200
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
VPA-MVSNet		80.60 189	80.55 166	80.76 280	88.07 202	60.80 330	86.86 215	91.58 136	75.67 114	80.24 178	89.45 210	63.34 172	90.25 319	70.51 242	79.22 314	91.23 213

MVS_Test		83.15 128	83.06 120	83.41 193	86.86 263	63.21 286	86.11 247	92.00 112	74.31 157	82.87 131	89.44 211	70.03 87	93.21 196	77.39 160	88.50 158	93.81 93

EI-MVSNet-UG-set		83.81 104	83.38 115	85.09 103	87.87 211	67.53 166	87.44 194	89.66 200	79.74 18	82.23 141	89.41 212	70.24 82	94.74 114	79.95 123	83.92 245	92.99 146

RPSCF		73.23 342	71.46 344	78.54 332	82.50 379	59.85 343	82.18 343	82.84 369	58.96 419	71.15 355	89.41 212	45.48 391	84.77 397	58.82 358	71.83 405	91.02 222

UniMVSNet_NR-MVSNet		81.88 151	81.54 150	82.92 217	88.46 184	63.46 280	87.13 202	92.37 86	80.19 12	78.38 211	89.14 214	71.66 64	93.05 210	70.05 248	76.46 348	92.25 177

tttt0517		79.40 220	77.91 234	83.90 177	88.10 200	63.84 265	88.37 157	84.05 344	71.45 224	76.78 250	89.12 215	49.93 346	94.89 105	70.18 247	83.18 264	92.96 147

DU-MVS		81.12 171	80.52 167	82.90 218	87.80 215	63.46 280	87.02 207	91.87 120	79.01 31	78.38 211	89.07 216	65.02 159	93.05 210	70.05 248	76.46 348	92.20 180

NR-MVSNet		80.23 202	79.38 199	82.78 228	87.80 215	63.34 283	86.31 239	91.09 152	79.01 31	72.17 343	89.07 216	67.20 131	92.81 222	66.08 287	75.65 361	92.20 180

icg_test_0407_2		78.92 235	78.93 212	78.90 324	87.13 253	63.59 273	76.58 418	89.33 213	70.51 251	77.82 224	89.03 218	61.84 201	81.38 422	72.56 221	85.56 219	91.74 195

IMVS_0407		80.61 187	79.90 184	82.75 231	87.13 253	63.59 273	85.33 269	89.33 213	70.51 251	77.82 224	89.03 218	61.84 201	92.91 215	72.56 221	85.56 219	91.74 195

IMVS_0404		77.16 279	76.42 277	79.37 315	87.13 253	63.59 273	77.12 416	89.33 213	70.51 251	66.22 416	89.03 218	50.36 338	82.78 412	72.56 221	85.56 219	91.74 195

IMVS_0403		80.80 180	80.12 179	82.87 220	87.13 253	63.59 273	85.19 270	89.33 213	70.51 251	78.49 208	89.03 218	63.26 175	93.27 191	72.56 221	85.56 219	91.74 195

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 267	93.44 32	78.70 34	83.63 115	89.03 218	74.57 27	95.71 66	80.26 121	94.04 67	93.66 101
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
mvsmamba		80.60 189	79.38 199	84.27 147	89.74 128	67.24 178	87.47 187	86.95 298	70.02 265	75.38 285	88.93 223	51.24 327	92.56 230	75.47 189	89.22 143	93.00 145

baseline1		76.98 282	76.75 270	77.66 351	88.13 198	55.66 402	85.12 274	81.89 377	73.04 196	76.79 249	88.90 224	62.43 192	87.78 364	63.30 308	71.18 409	89.55 287

DP-MVS		76.78 286	74.57 305	83.42 191	93.29 52	69.46 104	88.55 149	83.70 348	63.98 371	70.20 361	88.89 225	54.01 291	94.80 111	46.66 437	81.88 280	86.01 386

ab-mvs		79.51 214	78.97 211	81.14 270	88.46 184	60.91 328	83.84 311	89.24 225	70.36 256	79.03 195	88.87 226	63.23 177	90.21 320	65.12 294	82.57 272	92.28 176

PEN-MVS		77.73 265	77.69 246	77.84 347	87.07 261	53.91 419	87.91 175	91.18 147	77.56 51	73.14 328	88.82 227	61.23 217	89.17 340	59.95 344	72.37 399	90.43 246

tt0805		78.73 238	77.83 238	81.43 259	85.17 308	60.30 339	89.41 107	90.90 156	71.21 230	77.17 244	88.73 228	46.38 376	93.21 196	72.57 219	78.96 315	90.79 229

test_djsdf		80.30 201	79.32 202	83.27 197	83.98 337	65.37 219	90.50 72	90.38 173	68.55 305	76.19 266	88.70 229	56.44 269	93.46 183	78.98 140	80.14 302	90.97 223

PAPM		77.68 269	76.40 278	81.51 257	87.29 249	61.85 314	83.78 312	89.59 204	64.74 357	71.23 353	88.70 229	62.59 188	93.66 165	52.66 402	87.03 190	89.01 303

DTE-MVSNet		76.99 281	76.80 266	77.54 356	86.24 280	53.06 428	87.52 185	90.66 164	77.08 69	72.50 337	88.67 231	60.48 232	89.52 332	57.33 373	70.74 411	90.05 268

PS-CasMVS		78.01 259	78.09 230	77.77 349	87.71 224	54.39 416	88.02 169	91.22 145	77.50 54	73.26 326	88.64 232	60.73 224	88.41 356	61.88 328	73.88 388	90.53 242

cdsmvs_eth3d_5k		19.96 454	26.61 456	0.00 475	0.00 498	0.00 500	0.00 487	89.26 222	0.00 493	0.00 494	88.61 233	61.62 207	0.00 494	0.00 493	0.00 492	0.00 490

lupinMVS		81.39 166	80.27 174	84.76 119	87.35 240	70.21 86	85.55 263	86.41 310	62.85 383	81.32 157	88.61 233	61.68 205	92.24 247	78.41 147	90.26 123	91.83 192

F-COLMAP		76.38 297	74.33 311	82.50 237	89.28 149	66.95 186	88.41 153	89.03 234	64.05 369	66.83 405	88.61 233	46.78 372	92.89 216	57.48 370	78.55 317	87.67 341

mvs_anonymous		79.42 219	79.11 208	80.34 289	84.45 328	57.97 363	82.59 337	87.62 279	67.40 320	76.17 269	88.56 236	68.47 115	89.59 331	70.65 241	86.05 208	93.47 116

CP-MVSNet		78.22 250	78.34 224	77.84 347	87.83 214	54.54 414	87.94 173	91.17 148	77.65 46	73.48 324	88.49 237	62.24 196	88.43 355	62.19 324	74.07 384	90.55 241

PVSNet_Blended_VisFu		82.62 138	81.83 148	84.96 107	90.80 101	69.76 97	88.74 140	91.70 129	69.39 281	78.96 196	88.46 238	65.47 155	94.87 107	74.42 198	88.57 155	90.24 255

CANet_DTU		80.61 187	79.87 185	82.83 221	85.60 297	63.17 289	87.36 196	88.65 255	76.37 94	75.88 272	88.44 239	53.51 295	93.07 208	73.30 210	89.74 134	92.25 177

PLC	70.83 11	78.05 257	76.37 279	83.08 208	91.88 83	67.80 156	88.19 163	89.46 208	64.33 364	69.87 370	88.38 240	53.66 293	93.58 166	58.86 357	82.73 269	87.86 338
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 215	79.22 206	80.27 291	88.79 172	58.35 356	85.06 277	88.61 257	78.56 35	77.65 229	88.34 241	63.81 171	90.66 314	64.98 296	77.22 336	91.80 194

XXY-MVS		75.41 311	75.56 288	74.96 382	83.59 348	57.82 367	80.59 368	83.87 347	66.54 333	74.93 304	88.31 242	63.24 176	80.09 428	62.16 325	76.85 342	86.97 367

Effi-MVS+		83.62 115	83.08 119	85.24 95	88.38 188	67.45 167	88.89 129	89.15 229	75.50 117	82.27 140	88.28 243	69.61 94	94.45 127	77.81 153	87.84 173	93.84 91

API-MVS		81.99 149	81.23 153	84.26 149	90.94 97	70.18 91	91.10 63	89.32 217	71.51 223	78.66 203	88.28 243	65.26 156	95.10 97	64.74 298	91.23 107	87.51 347

thisisatest0530		79.40 220	77.76 243	84.31 141	87.69 228	65.10 230	87.36 196	84.26 342	70.04 264	77.42 233	88.26 245	49.94 344	94.79 112	70.20 246	84.70 231	93.03 142

hse-mvs2		81.72 154	80.94 159	84.07 160	88.72 175	67.68 160	85.87 253	87.26 291	76.02 104	84.67 87	88.22 246	61.54 208	93.48 181	82.71 96	73.44 393	91.06 218

xiu_mvs_v1_base_debu		80.80 180	79.72 191	84.03 168	87.35 240	70.19 88	85.56 260	88.77 246	69.06 294	81.83 146	88.16 247	50.91 330	92.85 218	78.29 149	87.56 178	89.06 298

xiu_mvs_v1_base		80.80 180	79.72 191	84.03 168	87.35 240	70.19 88	85.56 260	88.77 246	69.06 294	81.83 146	88.16 247	50.91 330	92.85 218	78.29 149	87.56 178	89.06 298

xiu_mvs_v1_base_debi		80.80 180	79.72 191	84.03 168	87.35 240	70.19 88	85.56 260	88.77 246	69.06 294	81.83 146	88.16 247	50.91 330	92.85 218	78.29 149	87.56 178	89.06 298

UniMVSNet (Re)		81.60 159	81.11 155	83.09 206	88.38 188	64.41 254	87.60 183	93.02 50	78.42 37	78.56 206	88.16 247	69.78 91	93.26 192	69.58 255	76.49 347	91.60 200

AUN-MVS		79.21 226	77.60 248	84.05 166	88.71 176	67.61 162	85.84 255	87.26 291	69.08 293	77.23 239	88.14 251	53.20 299	93.47 182	75.50 188	73.45 392	91.06 218

Anonymous20231211		78.97 233	77.69 246	82.81 223	90.54 106	64.29 256	90.11 83	91.51 138	65.01 355	76.16 270	88.13 252	50.56 335	93.03 213	69.68 254	77.56 334	91.11 216

pm-mvs1		77.25 278	76.68 272	78.93 323	84.22 331	58.62 354	86.41 233	88.36 260	71.37 225	73.31 325	88.01 253	61.22 218	89.15 341	64.24 302	73.01 396	89.03 302

LuminaMVS		80.68 185	79.62 194	83.83 178	85.07 314	68.01 148	86.99 208	88.83 243	70.36 256	81.38 156	87.99 254	50.11 341	92.51 234	79.02 137	86.89 193	90.97 223

SD_0403		74.65 319	74.77 303	74.29 391	86.20 282	47.42 455	83.71 314	85.12 328	69.30 284	68.50 385	87.95 255	59.40 240	86.05 381	49.38 422	83.35 260	89.40 290

LTVRE_ROB	69.57 13	76.25 298	74.54 307	81.41 260	88.60 179	64.38 255	79.24 387	89.12 232	70.76 244	69.79 372	87.86 256	49.09 356	93.20 199	56.21 385	80.16 300	86.65 375
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
testing3-2		75.12 316	75.19 298	74.91 383	90.40 109	45.09 466	80.29 374	78.42 418	78.37 40	76.54 258	87.75 257	44.36 397	87.28 370	57.04 376	83.49 257	92.37 171

WTY-MVS		75.65 306	75.68 285	75.57 373	86.40 278	56.82 382	77.92 410	82.40 372	65.10 352	76.18 267	87.72 258	63.13 182	80.90 425	60.31 342	81.96 278	89.00 305

TAMVS		78.89 236	77.51 252	83.03 211	87.80 215	67.79 157	84.72 284	85.05 331	67.63 315	76.75 251	87.70 259	62.25 195	90.82 308	58.53 361	87.13 188	90.49 244

BH-untuned		79.47 216	78.60 217	82.05 246	89.19 154	65.91 203	86.07 248	88.52 258	72.18 209	75.42 283	87.69 260	61.15 219	93.54 173	60.38 341	86.83 194	86.70 373

COLMAP_ROB	66.92 17	73.01 345	70.41 362	80.81 279	87.13 253	65.63 211	88.30 160	84.19 343	62.96 381	63.80 434	87.69 260	38.04 437	92.56 230	46.66 437	74.91 378	84.24 413
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 322	72.42 335	79.80 304	83.76 343	59.59 347	85.92 252	86.64 306	66.39 334	66.96 403	87.58 262	39.46 427	91.60 270	65.76 290	69.27 417	88.22 331

FA-MVS(test-final)		80.96 173	79.91 183	84.10 154	88.30 191	65.01 231	84.55 291	90.01 188	73.25 190	79.61 185	87.57 263	58.35 249	94.72 115	71.29 234	86.25 204	92.56 161

Baseline_NR-MVSNet		78.15 254	78.33 225	77.61 353	85.79 291	56.21 395	86.78 219	85.76 322	73.60 177	77.93 223	87.57 263	65.02 159	88.99 343	67.14 279	75.33 372	87.63 342

WR-MVS_H		78.51 245	78.49 219	78.56 331	88.02 204	56.38 391	88.43 151	92.67 72	77.14 65	73.89 318	87.55 265	66.25 144	89.24 338	58.92 356	73.55 391	90.06 267

EI-MVSNet		80.52 193	79.98 181	82.12 243	84.28 329	63.19 288	86.41 233	88.95 240	74.18 162	78.69 201	87.54 266	66.62 137	92.43 237	72.57 219	80.57 296	90.74 233

CVMVSNet		72.99 346	72.58 333	74.25 392	84.28 329	50.85 444	86.41 233	83.45 354	44.56 464	73.23 327	87.54 266	49.38 351	85.70 385	65.90 288	78.44 320	86.19 381

ACMH+	68.96 14	76.01 302	74.01 313	82.03 247	88.60 179	65.31 223	88.86 130	87.55 280	70.25 262	67.75 392	87.47 268	41.27 418	93.19 201	58.37 363	75.94 358	87.60 343

TransMVSNet (Re)		75.39 313	74.56 306	77.86 346	85.50 301	57.10 379	86.78 219	86.09 318	72.17 210	71.53 350	87.34 269	63.01 183	89.31 336	56.84 379	61.83 446	87.17 359

GBi-Net		78.40 246	77.40 253	81.40 261	87.60 231	63.01 290	88.39 154	89.28 219	71.63 218	75.34 287	87.28 270	54.80 279	91.11 296	62.72 315	79.57 306	90.09 263

test1		78.40 246	77.40 253	81.40 261	87.60 231	63.01 290	88.39 154	89.28 219	71.63 218	75.34 287	87.28 270	54.80 279	91.11 296	62.72 315	79.57 306	90.09 263

FMVSNet2		78.20 252	77.21 257	81.20 268	87.60 231	62.89 296	87.47 187	89.02 235	71.63 218	75.29 293	87.28 270	54.80 279	91.10 299	62.38 321	79.38 311	89.61 285

FMVSNet1		77.44 273	76.12 281	81.40 261	86.81 266	63.01 290	88.39 154	89.28 219	70.49 255	74.39 313	87.28 270	49.06 357	91.11 296	60.91 337	78.52 318	90.09 263

v2v482		80.23 202	79.29 203	83.05 210	83.62 347	64.14 258	87.04 205	89.97 189	73.61 176	78.18 217	87.22 274	61.10 220	93.82 156	76.11 177	76.78 344	91.18 214

ITE_SJBPF				78.22 338	81.77 389	60.57 334		83.30 355	69.25 287	67.54 394	87.20 275	36.33 444	87.28 370	54.34 393	74.62 381	86.80 370

anonymousdsp		78.60 242	77.15 258	82.98 215	80.51 408	67.08 181	87.24 201	89.53 206	65.66 343	75.16 296	87.19 276	52.52 301	92.25 246	77.17 162	79.34 312	89.61 285

MVSTER		79.01 231	77.88 237	82.38 239	83.07 362	64.80 243	84.08 309	88.95 240	69.01 297	78.69 201	87.17 277	54.70 283	92.43 237	74.69 194	80.57 296	89.89 276

thres100view900		76.50 290	75.55 289	79.33 316	89.52 133	56.99 380	85.83 256	83.23 357	73.94 167	76.32 263	87.12 278	51.89 317	91.95 257	48.33 428	83.75 249	89.07 296

thres600view7		76.50 290	75.44 290	79.68 309	89.40 141	57.16 377	85.53 265	83.23 357	73.79 171	76.26 264	87.09 279	51.89 317	91.89 260	48.05 433	83.72 252	90.00 269

XVG-ACMP-BASELINE		76.11 300	74.27 312	81.62 254	83.20 358	64.67 245	83.60 319	89.75 198	69.75 275	71.85 346	87.09 279	32.78 451	92.11 250	69.99 250	80.43 298	88.09 334

HY-MVS	69.67 12	77.95 260	77.15 258	80.36 288	87.57 239	60.21 341	83.37 325	87.78 276	66.11 336	75.37 286	87.06 281	63.27 174	90.48 316	61.38 334	82.43 273	90.40 248

CHOSEN 1792x2688		77.63 271	75.69 284	83.44 190	89.98 122	68.58 129	78.70 397	87.50 282	56.38 439	75.80 274	86.84 282	58.67 246	91.40 286	61.58 332	85.75 217	90.34 250

v8		79.97 208	79.02 210	82.80 224	84.09 334	64.50 251	87.96 171	90.29 180	74.13 164	75.24 294	86.81 283	62.88 186	93.89 155	74.39 199	75.40 370	90.00 269

AllTest		70.96 366	68.09 381	79.58 312	85.15 310	63.62 269	84.58 290	79.83 405	62.31 390	60.32 447	86.73 284	32.02 452	88.96 346	50.28 416	71.57 407	86.15 382

TestCases				79.58 312	85.15 310	63.62 269		79.83 405	62.31 390	60.32 447	86.73 284	32.02 452	88.96 346	50.28 416	71.57 407	86.15 382

LCM-MVSNet-Re		77.05 280	76.94 263	77.36 357	87.20 250	51.60 437	80.06 377	80.46 396	75.20 130	67.69 393	86.72 286	62.48 190	88.98 344	63.44 306	89.25 141	91.51 204

1112_ss		77.40 275	76.43 276	80.32 290	89.11 160	60.41 338	83.65 316	87.72 278	62.13 393	73.05 329	86.72 286	62.58 189	89.97 324	62.11 327	80.80 292	90.59 240

ab-mvs-re		7.23 457	9.64 460	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	86.72 286	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

IterMVS-LS		80.06 205	79.38 199	82.11 245	85.89 289	63.20 287	86.79 218	89.34 212	74.19 161	75.45 282	86.72 286	66.62 137	92.39 239	72.58 218	76.86 341	90.75 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 303	73.93 315	81.77 252	88.71 176	66.61 189	88.62 145	89.01 236	69.81 271	66.78 406	86.70 290	41.95 415	91.51 281	55.64 386	78.14 326	87.17 359
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 296	75.44 290	79.27 317	89.28 149	58.09 359	81.69 349	87.07 296	59.53 414	72.48 338	86.67 291	61.30 215	89.33 335	60.81 339	80.15 301	90.41 247

FMVSNet3		77.88 262	76.85 265	80.97 276	86.84 265	62.36 304	86.52 230	88.77 246	71.13 231	75.34 287	86.66 292	54.07 289	91.10 299	62.72 315	79.57 306	89.45 289

pmmvs6		74.69 318	73.39 322	78.61 328	81.38 397	57.48 374	86.64 225	87.95 270	64.99 356	70.18 362	86.61 293	50.43 337	89.52 332	62.12 326	70.18 414	88.83 312

ET-MVSNet_ETH3D		78.63 241	76.63 273	84.64 122	86.73 269	69.47 102	85.01 278	84.61 335	69.54 279	66.51 413	86.59 294	50.16 340	91.75 265	76.26 175	84.24 241	92.69 157

testgi		66.67 406	66.53 402	67.08 441	75.62 447	41.69 476	75.93 421	76.50 433	66.11 336	65.20 424	86.59 294	35.72 446	74.71 460	43.71 449	73.38 394	84.84 407

CLD-MVS		82.31 143	81.65 149	84.29 144	88.47 183	67.73 158	85.81 257	92.35 87	75.78 109	78.33 213	86.58 296	64.01 168	94.35 128	76.05 179	87.48 181	90.79 229
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 210	78.67 215	82.97 216	84.06 335	64.95 234	87.88 177	90.62 165	73.11 194	75.11 298	86.56 297	61.46 211	94.05 143	73.68 204	75.55 363	89.90 275

CDS-MVSNet		79.07 230	77.70 245	83.17 203	87.60 231	68.23 141	84.40 300	86.20 315	67.49 318	76.36 262	86.54 298	61.54 208	90.79 309	61.86 329	87.33 183	90.49 244
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 156	81.05 156	83.60 184	89.15 155	68.03 147	84.46 294	90.02 187	70.67 245	81.30 160	86.53 299	63.17 178	94.19 138	75.60 186	88.54 156	88.57 323

TR-MVS		77.44 273	76.18 280	81.20 268	88.24 192	63.24 285	84.61 289	86.40 311	67.55 317	77.81 226	86.48 300	54.10 288	93.15 203	57.75 369	82.72 270	87.20 358

EIA-MVS		83.31 126	82.80 126	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 149	78.96 196	86.42 301	69.06 106	95.26 87	75.54 187	90.09 126	93.62 108

tfpn200view9		76.42 295	75.37 294	79.55 314	89.13 156	57.65 371	85.17 271	83.60 349	73.41 184	76.45 259	86.39 302	52.12 308	91.95 257	48.33 428	83.75 249	89.07 296

thres400		76.50 290	75.37 294	79.86 302	89.13 156	57.65 371	85.17 271	83.60 349	73.41 184	76.45 259	86.39 302	52.12 308	91.95 257	48.33 428	83.75 249	90.00 269

v7n		78.97 233	77.58 249	83.14 204	83.45 351	65.51 214	88.32 159	91.21 146	73.69 174	72.41 339	86.32 304	57.93 251	93.81 157	69.18 258	75.65 361	90.11 261

MAR-MVS		81.84 152	80.70 162	85.27 94	91.32 89	71.53 58	89.82 88	90.92 155	69.77 274	78.50 207	86.21 305	62.36 193	94.52 123	65.36 292	92.05 93	89.77 281
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
v1144		80.03 206	79.03 209	83.01 212	83.78 342	64.51 249	87.11 204	90.57 168	71.96 214	78.08 220	86.20 306	61.41 212	93.94 147	74.93 193	77.23 335	90.60 239

test_vis1_n_1920		75.52 308	75.78 283	74.75 387	79.84 416	57.44 375	83.26 327	85.52 324	62.83 384	79.34 193	86.17 307	45.10 392	79.71 429	78.75 142	81.21 286	87.10 365

V42		79.38 222	78.24 227	82.83 221	81.10 402	65.50 215	85.55 263	89.82 193	71.57 222	78.21 215	86.12 308	60.66 228	93.18 202	75.64 184	75.46 367	89.81 280

PVSNet_BlendedMVS		80.60 189	80.02 180	82.36 240	88.85 163	65.40 216	86.16 246	92.00 112	69.34 283	78.11 218	86.09 309	66.02 150	94.27 131	71.52 230	82.06 277	87.39 349

v1192		79.59 213	78.43 222	83.07 209	83.55 349	64.52 248	86.93 212	90.58 166	70.83 241	77.78 227	85.90 310	59.15 242	93.94 147	73.96 203	77.19 337	90.76 231

SixPastTwentyTwo		73.37 336	71.26 350	79.70 308	85.08 313	57.89 365	85.57 259	83.56 351	71.03 237	65.66 418	85.88 311	42.10 413	92.57 229	59.11 354	63.34 441	88.65 320

EPNet_dtu		75.46 309	74.86 301	77.23 360	82.57 378	54.60 413	86.89 213	83.09 361	71.64 217	66.25 415	85.86 312	55.99 271	88.04 360	54.92 390	86.55 198	89.05 301
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 332	73.64 320	73.51 399	82.80 372	55.01 410	76.12 420	81.69 380	62.47 389	74.68 308	85.85 313	57.32 259	78.11 436	60.86 338	80.93 288	87.39 349

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	85.71 314	69.32 98	95.38 82	80.82 113	91.37 105	92.72 154

test_cas_vis1_n_1920		73.76 330	73.74 319	73.81 397	75.90 443	59.77 344	80.51 369	82.40 372	58.30 425	81.62 154	85.69 315	44.35 398	76.41 447	76.29 174	78.61 316	85.23 399

v1240		78.99 232	77.78 241	82.64 233	83.21 357	63.54 277	86.62 226	90.30 179	69.74 277	77.33 235	85.68 316	57.04 263	93.76 161	73.13 213	76.92 339	90.62 237

v144192		79.47 216	78.37 223	82.78 228	83.35 352	63.96 261	86.96 209	90.36 176	69.99 267	77.50 231	85.67 317	60.66 228	93.77 160	74.27 200	76.58 345	90.62 237

tfpnnormal		74.39 320	73.16 326	78.08 342	86.10 287	58.05 360	84.65 288	87.53 281	70.32 259	71.22 354	85.63 318	54.97 277	89.86 325	43.03 452	75.02 377	86.32 378

PS-MVSNAJ		81.69 156	81.02 157	83.70 182	89.51 134	68.21 142	84.28 302	90.09 186	70.79 242	81.26 161	85.62 319	63.15 179	94.29 129	75.62 185	88.87 149	88.59 322

SSC-MVS3.2		73.35 339	73.39 322	73.23 400	85.30 306	49.01 451	74.58 435	81.57 381	75.21 129	73.68 321	85.58 320	52.53 300	82.05 417	54.33 394	77.69 332	88.63 321

v1921920		79.22 225	78.03 231	82.80 224	83.30 354	63.94 263	86.80 217	90.33 177	69.91 270	77.48 232	85.53 321	58.44 248	93.75 162	73.60 205	76.85 342	90.71 235

test_0402		72.79 350	70.44 361	79.84 303	88.13 198	65.99 201	85.93 251	84.29 340	65.57 344	67.40 399	85.49 322	46.92 369	92.61 226	35.88 466	74.38 383	80.94 445

v148		78.72 239	77.80 240	81.47 258	82.73 374	61.96 313	86.30 240	88.08 264	73.26 189	76.18 267	85.47 323	62.46 191	92.36 241	71.92 229	73.82 389	90.09 263

USDC		70.33 375	68.37 376	76.21 367	80.60 406	56.23 394	79.19 389	86.49 309	60.89 401	61.29 442	85.47 323	31.78 454	89.47 334	53.37 399	76.21 356	82.94 431

VortexMVS		78.57 244	77.89 236	80.59 283	85.89 289	62.76 297	85.61 258	89.62 203	72.06 212	74.99 302	85.38 325	55.94 272	90.77 312	74.99 192	76.58 345	88.23 330

MVP-Stereo		76.12 299	74.46 309	81.13 271	85.37 304	69.79 95	84.42 299	87.95 270	65.03 354	67.46 396	85.33 326	53.28 298	91.73 267	58.01 367	83.27 262	81.85 440
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 253	76.99 262	81.78 251	85.66 294	66.99 182	84.66 286	90.47 170	55.08 444	72.02 345	85.27 327	63.83 170	94.11 141	66.10 286	89.80 133	84.24 413

DIV-MVS_self_test		77.72 266	76.76 268	80.58 284	82.48 381	60.48 336	83.09 331	87.86 273	69.22 288	74.38 314	85.24 328	62.10 198	91.53 279	71.09 235	75.40 370	89.74 282

FE-MVS		77.78 264	75.68 285	84.08 159	88.09 201	66.00 200	83.13 330	87.79 275	68.42 309	78.01 221	85.23 329	45.50 390	95.12 92	59.11 354	85.83 216	91.11 216

cl____		77.72 266	76.76 268	80.58 284	82.49 380	60.48 336	83.09 331	87.87 272	69.22 288	74.38 314	85.22 330	62.10 198	91.53 279	71.09 235	75.41 369	89.73 283

HyFIR lowres test		77.53 272	75.40 292	83.94 176	89.59 130	66.62 188	80.36 372	88.64 256	56.29 440	76.45 259	85.17 331	57.64 255	93.28 189	61.34 335	83.10 265	91.91 191

pmmvs4		74.03 328	71.91 339	80.39 287	81.96 386	68.32 135	81.45 353	82.14 374	59.32 415	69.87 370	85.13 332	52.40 304	88.13 359	60.21 343	74.74 380	84.73 409

TDRefinement		67.49 398	64.34 410	76.92 362	73.47 459	61.07 325	84.86 282	82.98 365	59.77 411	58.30 454	85.13 332	26.06 462	87.89 362	47.92 434	60.59 451	81.81 441

Fast-Effi-MVS+		80.81 177	79.92 182	83.47 188	88.85 163	64.51 249	85.53 265	89.39 211	70.79 242	78.49 208	85.06 334	67.54 127	93.58 166	67.03 281	86.58 197	92.32 174

PVSNet_Blended		80.98 172	80.34 171	82.90 218	88.85 163	65.40 216	84.43 297	92.00 112	67.62 316	78.11 218	85.05 335	66.02 150	94.27 131	71.52 230	89.50 138	89.01 303

ttmdpeth		59.91 426	57.10 430	68.34 436	67.13 473	46.65 460	74.64 434	67.41 463	48.30 459	62.52 440	85.04 336	20.40 472	75.93 452	42.55 454	45.90 474	82.44 434

test_fmvs1_n		70.86 368	70.24 364	72.73 408	72.51 466	55.28 407	81.27 357	79.71 407	51.49 455	78.73 200	84.87 337	27.54 461	77.02 441	76.06 178	79.97 304	85.88 390

WBMVS		73.43 334	72.81 330	75.28 379	87.91 209	50.99 443	78.59 400	81.31 386	65.51 347	74.47 312	84.83 338	46.39 375	86.68 374	58.41 362	77.86 328	88.17 333

CMPMVS	51.72 21	70.19 377	68.16 379	76.28 366	73.15 462	57.55 373	79.47 384	83.92 345	48.02 460	56.48 460	84.81 339	43.13 405	86.42 378	62.67 318	81.81 281	84.89 406
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 393	67.61 392	71.31 420	78.51 430	47.01 458	84.47 292	84.27 341	42.27 467	66.44 414	84.79 340	40.44 423	83.76 403	58.76 359	68.54 422	83.17 425

BH-w/o		78.21 251	77.33 256	80.84 278	88.81 167	65.13 227	84.87 281	87.85 274	69.75 275	74.52 311	84.74 341	61.34 214	93.11 206	58.24 365	85.84 215	84.27 412

pmmvs5		71.55 361	70.20 365	75.61 372	77.83 434	56.39 390	81.74 347	80.89 387	57.76 430	67.46 396	84.49 342	49.26 354	85.32 392	57.08 375	75.29 373	85.11 403

reproduce_monomvs		75.40 312	74.38 310	78.46 336	83.92 339	57.80 368	83.78 312	86.94 299	73.47 182	72.25 342	84.47 343	38.74 432	89.27 337	75.32 190	70.53 412	88.31 328

thres200		75.55 307	74.47 308	78.82 325	87.78 218	57.85 366	83.07 333	83.51 352	72.44 205	75.84 273	84.42 344	52.08 311	91.75 265	47.41 435	83.64 254	86.86 369

test_fmvs1		70.93 367	70.52 359	72.16 412	73.71 455	55.05 409	80.82 360	78.77 416	51.21 456	78.58 205	84.41 345	31.20 456	76.94 442	75.88 182	80.12 303	84.47 411

testing3		68.56 392	67.67 391	71.22 421	87.33 245	42.87 471	83.06 334	71.54 451	70.36 256	69.08 378	84.38 346	30.33 458	85.69 386	37.50 464	75.45 368	85.09 404

test_fmvs2		68.35 395	67.48 394	70.98 423	69.50 469	51.95 432	80.05 378	76.38 434	49.33 458	74.65 309	84.38 346	23.30 470	75.40 458	74.51 197	75.17 376	85.60 393

eth_miper_zixun_eth		77.92 261	76.69 271	81.61 256	83.00 365	61.98 312	83.15 329	89.20 227	69.52 280	74.86 305	84.35 348	61.76 204	92.56 230	71.50 232	72.89 397	90.28 254

myMVS_eth3d28		73.62 331	73.53 321	73.90 396	88.20 193	47.41 456	78.06 407	79.37 410	74.29 159	73.98 317	84.29 349	44.67 393	83.54 406	51.47 408	87.39 182	90.74 233

testing91		76.54 288	75.66 287	79.18 320	88.43 186	55.89 398	81.08 358	83.00 364	73.76 172	75.34 287	84.29 349	46.20 381	90.07 322	64.33 300	84.50 233	91.58 202

c3_l		78.75 237	77.91 234	81.26 266	82.89 371	61.56 318	84.09 308	89.13 231	69.97 268	75.56 277	84.29 349	66.36 142	92.09 251	73.47 208	75.48 365	90.12 260

testing99		76.09 301	75.12 300	79.00 321	88.16 195	55.50 404	80.79 362	81.40 384	73.30 188	75.17 295	84.27 352	44.48 396	90.02 323	64.28 301	84.22 242	91.48 207

UWE-MVS		72.13 358	71.49 343	74.03 394	86.66 272	47.70 453	81.40 355	76.89 432	63.60 375	75.59 276	84.22 353	39.94 425	85.62 387	48.98 425	86.13 207	88.77 315

FE-MVSNET3		76.43 294	75.32 296	79.76 306	83.00 365	60.72 331	81.74 347	88.76 250	68.99 298	72.98 330	84.19 354	56.41 270	90.27 317	62.39 320	79.40 310	88.31 328

Fast-Effi-MVS+-dtu		78.02 258	76.49 274	82.62 234	83.16 361	66.96 185	86.94 211	87.45 284	72.45 203	71.49 351	84.17 355	54.79 282	91.58 271	67.61 272	80.31 299	89.30 294

IterMVS-SCA-FT		75.43 310	73.87 317	80.11 297	82.69 375	64.85 242	81.57 351	83.47 353	69.16 291	70.49 358	84.15 356	51.95 314	88.15 358	69.23 257	72.14 403	87.34 353

1314		76.53 289	75.30 297	80.21 294	83.93 338	62.32 306	84.66 286	88.81 244	60.23 407	70.16 364	84.07 357	55.30 276	90.73 313	67.37 275	83.21 263	87.59 345

cl22		78.07 256	77.01 260	81.23 267	82.37 383	61.83 315	83.55 320	87.98 268	68.96 299	75.06 300	83.87 358	61.40 213	91.88 261	73.53 206	76.39 350	89.98 272

EG-PatchMatch MVS		74.04 326	71.82 340	80.71 281	84.92 316	67.42 168	85.86 254	88.08 264	66.04 338	64.22 429	83.85 359	35.10 447	92.56 230	57.44 371	80.83 291	82.16 438

thisisatest0515		77.33 276	75.38 293	83.18 202	85.27 307	63.80 266	82.11 344	83.27 356	65.06 353	75.91 271	83.84 360	49.54 348	94.27 131	67.24 277	86.19 205	91.48 207

test20.03		67.45 399	66.95 400	68.94 430	75.48 448	44.84 467	77.50 412	77.67 422	66.66 327	63.01 436	83.80 361	47.02 368	78.40 434	42.53 455	68.86 421	83.58 422

miper_ehance_all_eth		78.59 243	77.76 243	81.08 272	82.66 376	61.56 318	83.65 316	89.15 229	68.87 300	75.55 278	83.79 362	66.49 140	92.03 252	73.25 211	76.39 350	89.64 284

MSDG		73.36 338	70.99 353	80.49 286	84.51 327	65.80 207	80.71 366	86.13 317	65.70 342	65.46 419	83.74 363	44.60 394	90.91 307	51.13 411	76.89 340	84.74 408

MonoMVSNet		76.49 293	75.80 282	78.58 330	81.55 393	58.45 355	86.36 238	86.22 314	74.87 144	74.73 307	83.73 364	51.79 320	88.73 349	70.78 237	72.15 402	88.55 324

testing11		75.14 315	74.01 313	78.53 333	88.16 195	56.38 391	80.74 365	80.42 398	70.67 245	72.69 336	83.72 365	43.61 403	89.86 325	62.29 323	83.76 248	89.36 292

IterMVS		74.29 321	72.94 329	78.35 337	81.53 394	63.49 279	81.58 350	82.49 371	68.06 313	69.99 367	83.69 366	51.66 322	85.54 388	65.85 289	71.64 406	86.01 386
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 353	71.71 341	74.35 390	82.19 384	52.00 431	79.22 388	77.29 428	64.56 359	72.95 332	83.68 367	51.35 323	83.26 410	58.33 364	75.80 359	87.81 339

UWE-MVS-28		65.32 413	64.93 407	66.49 442	78.70 428	38.55 479	77.86 411	64.39 471	62.00 395	64.13 430	83.60 368	41.44 416	76.00 451	31.39 471	80.89 289	84.92 405

sc_t1		72.19 357	69.51 368	80.23 293	84.81 318	61.09 324	84.68 285	80.22 402	60.70 403	71.27 352	83.58 369	36.59 442	89.24 338	60.41 340	63.31 442	90.37 249

testing222		74.04 326	72.66 332	78.19 339	87.89 210	55.36 405	81.06 359	79.20 413	71.30 228	74.65 309	83.57 370	39.11 431	88.67 351	51.43 410	85.75 217	90.53 242

Effi-MVS+-dtu		80.03 206	78.57 218	84.42 133	85.13 312	68.74 121	88.77 136	88.10 263	74.99 136	74.97 303	83.49 371	57.27 260	93.36 187	73.53 206	80.88 290	91.18 214

baseline2		75.70 305	73.83 318	81.30 264	83.26 355	61.79 316	82.57 338	80.65 391	66.81 323	66.88 404	83.42 372	57.86 253	92.19 248	63.47 305	79.57 306	89.91 274

mvs5depth		69.45 384	67.45 395	75.46 377	73.93 453	55.83 399	79.19 389	83.23 357	66.89 322	71.63 349	83.32 373	33.69 450	85.09 393	59.81 346	55.34 461	85.46 395

TinyColmap		67.30 401	64.81 408	74.76 386	81.92 388	56.68 386	80.29 374	81.49 383	60.33 405	56.27 461	83.22 374	24.77 466	87.66 366	45.52 445	69.47 416	79.95 450

mvsany_test1		62.30 422	61.26 426	65.41 444	69.52 468	54.86 411	66.86 463	49.78 484	46.65 461	68.50 385	83.21 375	49.15 355	66.28 476	56.93 378	60.77 449	75.11 460

test_vis1_n		69.85 382	69.21 371	71.77 414	72.66 465	55.27 408	81.48 352	76.21 435	52.03 452	75.30 292	83.20 376	28.97 459	76.22 449	74.60 196	78.41 324	83.81 419

CostFormer		75.24 314	73.90 316	79.27 317	82.65 377	58.27 358	80.80 361	82.73 370	61.57 397	75.33 291	83.13 377	55.52 274	91.07 302	64.98 296	78.34 325	88.45 325

MVStest1		56.63 430	52.76 436	68.25 437	61.67 479	53.25 427	71.67 444	68.90 461	38.59 472	50.59 468	83.05 378	25.08 464	70.66 469	36.76 465	38.56 475	80.83 446

WB-MVSnew		71.96 360	71.65 342	72.89 406	84.67 325	51.88 434	82.29 341	77.57 423	62.31 390	73.67 322	83.00 379	53.49 296	81.10 424	45.75 444	82.13 276	85.70 392

ETVMVS		72.25 356	71.05 352	75.84 369	87.77 220	51.91 433	79.39 385	74.98 439	69.26 286	73.71 320	82.95 380	40.82 422	86.14 380	46.17 441	84.43 238	89.47 288

miper_lstm_enhance		74.11 325	73.11 327	77.13 361	80.11 412	59.62 346	72.23 442	86.92 301	66.76 325	70.40 359	82.92 381	56.93 264	82.92 411	69.06 260	72.63 398	88.87 310

GA-MVS		76.87 284	75.17 299	81.97 249	82.75 373	62.58 298	81.44 354	86.35 313	72.16 211	74.74 306	82.89 382	46.20 381	92.02 254	68.85 263	81.09 287	91.30 212

K. test v3		71.19 363	68.51 375	79.21 319	83.04 364	57.78 369	84.35 301	76.91 431	72.90 199	62.99 437	82.86 383	39.27 428	91.09 301	61.65 331	52.66 464	88.75 316

MS-PatchMatch		73.83 329	72.67 331	77.30 359	83.87 340	66.02 198	81.82 345	84.66 334	61.37 400	68.61 382	82.82 384	47.29 365	88.21 357	59.27 351	84.32 240	77.68 455

lessismore_v0				78.97 322	81.01 403	57.15 378		65.99 466		61.16 443	82.82 384	39.12 430	91.34 288	59.67 347	46.92 471	88.43 326

D2MVS		74.82 317	73.21 325	79.64 311	79.81 417	62.56 300	80.34 373	87.35 286	64.37 363	68.86 379	82.66 386	46.37 377	90.10 321	67.91 270	81.24 285	86.25 379

Anonymous20231206		68.60 390	67.80 388	71.02 422	80.23 411	50.75 445	78.30 405	80.47 395	56.79 437	66.11 417	82.63 387	46.35 378	78.95 432	43.62 450	75.70 360	83.36 424

MIMVSNet		70.69 370	69.30 369	74.88 384	84.52 326	56.35 393	75.87 424	79.42 409	64.59 358	67.76 391	82.41 388	41.10 419	81.54 420	46.64 439	81.34 283	86.75 372

UBG		73.08 344	72.27 337	75.51 375	88.02 204	51.29 441	78.35 404	77.38 427	65.52 345	73.87 319	82.36 389	45.55 388	86.48 377	55.02 389	84.39 239	88.75 316

OpenMVS_ROB	64.09 19	70.56 372	68.19 378	77.65 352	80.26 409	59.41 350	85.01 278	82.96 366	58.76 422	65.43 420	82.33 390	37.63 439	91.23 292	45.34 447	76.03 357	82.32 435

miper_enhance_ethall		77.87 263	76.86 264	80.92 277	81.65 390	61.38 321	82.68 336	88.98 237	65.52 345	75.47 279	82.30 391	65.76 154	92.00 255	72.95 214	76.39 350	89.39 291

test0.0.03 1		68.00 397	67.69 390	68.90 431	77.55 437	47.43 454	75.70 425	72.95 450	66.66 327	66.56 409	82.29 392	48.06 362	75.87 453	44.97 448	74.51 382	83.41 423

PVSNet	64.34 18	72.08 359	70.87 356	75.69 371	86.21 281	56.44 389	74.37 436	80.73 390	62.06 394	70.17 363	82.23 393	42.86 407	83.31 409	54.77 391	84.45 237	87.32 354

MIMVSNet1		68.58 391	66.78 401	73.98 395	80.07 413	51.82 435	80.77 363	84.37 337	64.40 362	59.75 450	82.16 394	36.47 443	83.63 405	42.73 453	70.33 413	86.48 377

CL-MVSNet_self_test		72.37 353	71.46 344	75.09 381	79.49 423	53.53 421	80.76 364	85.01 332	69.12 292	70.51 357	82.05 395	57.92 252	84.13 401	52.27 404	66.00 431	87.60 343

tpm2		73.26 341	71.46 344	78.63 327	83.34 353	56.71 385	80.65 367	80.40 399	56.63 438	73.55 323	82.02 396	51.80 319	91.24 291	56.35 384	78.42 323	87.95 335

PatchMatch-RL		72.38 352	70.90 355	76.80 364	88.60 179	67.38 171	79.53 383	76.17 436	62.75 386	69.36 375	82.00 397	45.51 389	84.89 396	53.62 397	80.58 295	78.12 454

FE-MVSNET2		72.88 349	71.28 348	77.67 350	78.30 432	57.78 369	84.43 297	88.92 242	69.56 278	64.61 426	81.67 398	46.73 374	88.54 354	59.33 350	67.99 423	86.69 374

FMVSNet5		69.50 383	67.96 383	74.15 393	82.97 369	55.35 406	80.01 379	82.12 375	62.56 388	63.02 435	81.53 399	36.92 440	81.92 418	48.42 427	74.06 385	85.17 402

CR-MVSNet		73.37 336	71.27 349	79.67 310	81.32 400	65.19 225	75.92 422	80.30 400	59.92 410	72.73 334	81.19 400	52.50 302	86.69 373	59.84 345	77.71 330	87.11 363

Patchmtry		70.74 369	69.16 372	75.49 376	80.72 404	54.07 418	74.94 433	80.30 400	58.34 424	70.01 365	81.19 400	52.50 302	86.54 375	53.37 399	71.09 410	85.87 391

IB-MVS	68.01 15	75.85 304	73.36 324	83.31 195	84.76 320	66.03 197	83.38 324	85.06 330	70.21 263	69.40 374	81.05 402	45.76 386	94.66 118	65.10 295	75.49 364	89.25 295
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
cascas		76.72 287	74.64 304	82.99 213	85.78 292	65.88 204	82.33 340	89.21 226	60.85 402	72.74 333	81.02 403	47.28 366	93.75 162	67.48 274	85.02 225	89.34 293

LF4IMVS		64.02 418	62.19 422	69.50 428	70.90 467	53.29 426	76.13 419	77.18 429	52.65 450	58.59 452	80.98 404	23.55 469	76.52 445	53.06 401	66.66 427	78.68 453

Anonymous20240521		68.80 389	67.22 398	73.55 398	74.33 451	54.11 417	83.18 328	85.61 323	58.15 426	61.68 441	80.94 405	30.71 457	81.27 423	57.00 377	73.34 395	85.28 398

gm-plane-assit					81.40 396	53.83 420			62.72 387		80.94 405		92.39 239	63.40 307

UnsupCasMVSNet_eth		67.33 400	65.99 404	71.37 417	73.48 458	51.47 439	75.16 429	85.19 327	65.20 350	60.78 444	80.93 407	42.35 409	77.20 440	57.12 374	53.69 463	85.44 396

dmvs_re		71.14 364	70.58 358	72.80 407	81.96 386	59.68 345	75.60 426	79.34 411	68.55 305	69.27 377	80.72 408	49.42 350	76.54 444	52.56 403	77.79 329	82.19 437

MDTV_nov1_ep13			69.97 366		83.18 359	53.48 422	77.10 417	80.18 404	60.45 404	69.33 376	80.44 409	48.89 360	86.90 372	51.60 407	78.51 319

pmmvs-eth3d		70.50 373	67.83 387	78.52 334	77.37 439	66.18 195	81.82 345	81.51 382	58.90 420	63.90 433	80.42 410	42.69 408	86.28 379	58.56 360	65.30 437	83.11 427

tt0320		70.49 374	68.03 382	77.89 345	84.78 319	59.12 351	83.55 320	80.44 397	58.13 427	67.43 398	80.41 411	39.26 429	87.54 367	55.12 388	63.18 443	86.99 366

mmtdpeth		74.16 324	73.01 328	77.60 355	83.72 344	61.13 322	85.10 275	85.10 329	72.06 212	77.21 243	80.33 412	43.84 401	85.75 384	77.14 163	52.61 465	85.91 389

tt0320-xc		70.11 378	67.45 395	78.07 343	85.33 305	59.51 349	83.28 326	78.96 415	58.77 421	67.10 402	80.28 413	36.73 441	87.42 368	56.83 380	59.77 453	87.29 355

PM-MVS		66.41 408	64.14 411	73.20 403	73.92 454	56.45 388	78.97 393	64.96 470	63.88 373	64.72 425	80.24 414	19.84 474	83.44 408	66.24 283	64.52 439	79.71 451

SCA		74.22 323	72.33 336	79.91 301	84.05 336	62.17 308	79.96 380	79.29 412	66.30 335	72.38 340	80.13 415	51.95 314	88.60 352	59.25 352	77.67 333	88.96 307

Patchmatch-test		64.82 416	63.24 417	69.57 427	79.42 424	49.82 449	63.49 475	69.05 459	51.98 453	59.95 449	80.13 415	50.91 330	70.98 468	40.66 458	73.57 390	87.90 337

tpmrst		72.39 351	72.13 338	73.18 404	80.54 407	49.91 448	79.91 381	79.08 414	63.11 378	71.69 348	79.95 417	55.32 275	82.77 413	65.66 291	73.89 387	86.87 368

DSMNet-mixed		57.77 429	56.90 431	60.38 450	67.70 471	35.61 481	69.18 455	53.97 482	32.30 480	57.49 457	79.88 418	40.39 424	68.57 474	38.78 462	72.37 399	76.97 456

MDA-MVSNet-bldmvs		66.68 405	63.66 415	75.75 370	79.28 425	60.56 335	73.92 438	78.35 419	64.43 360	50.13 469	79.87 419	44.02 400	83.67 404	46.10 442	56.86 455	83.03 429

PatchmatchNet		73.12 343	71.33 347	78.49 335	83.18 359	60.85 329	79.63 382	78.57 417	64.13 365	71.73 347	79.81 420	51.20 328	85.97 383	57.40 372	76.36 355	88.66 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 402	65.33 406	73.02 405	75.86 444	52.54 429	80.26 376	80.56 393	63.80 374	60.39 445	79.70 421	41.41 417	84.66 399	43.34 451	62.62 444	81.86 439

Syy-MVS		68.05 396	67.85 385	68.67 434	84.68 322	40.97 477	78.62 398	73.08 448	66.65 330	66.74 407	79.46 422	52.11 310	82.30 415	32.89 469	76.38 353	82.75 432

myMVS_eth3d		67.02 403	66.29 403	69.21 429	84.68 322	42.58 472	78.62 398	73.08 448	66.65 330	66.74 407	79.46 422	31.53 455	82.30 415	39.43 461	76.38 353	82.75 432

ppachtmachnet_test		70.04 379	67.34 397	78.14 340	79.80 418	61.13 322	79.19 389	80.59 392	59.16 417	65.27 421	79.29 424	46.75 373	87.29 369	49.33 423	66.72 426	86.00 388

EPMVS		69.02 387	68.16 379	71.59 415	79.61 421	49.80 450	77.40 413	66.93 464	62.82 385	70.01 365	79.05 425	45.79 385	77.86 438	56.58 382	75.26 374	87.13 362

PMMVS		69.34 385	68.67 374	71.35 419	75.67 446	62.03 311	75.17 428	73.46 446	50.00 457	68.68 380	79.05 425	52.07 312	78.13 435	61.16 336	82.77 268	73.90 461

test-LLR		72.94 347	72.43 334	74.48 388	81.35 398	58.04 361	78.38 401	77.46 424	66.66 327	69.95 368	79.00 427	48.06 362	79.24 430	66.13 284	84.83 228	86.15 382

test-mter		71.41 362	70.39 363	74.48 388	81.35 398	58.04 361	78.38 401	77.46 424	60.32 406	69.95 368	79.00 427	36.08 445	79.24 430	66.13 284	84.83 228	86.15 382

KD-MVS_self_test		68.81 388	67.59 393	72.46 411	74.29 452	45.45 461	77.93 409	87.00 297	63.12 377	63.99 432	78.99 429	42.32 410	84.77 397	56.55 383	64.09 440	87.16 361

test_fmvs3		63.36 420	61.82 423	67.98 438	62.51 478	46.96 459	77.37 414	74.03 445	45.24 463	67.50 395	78.79 430	12.16 482	72.98 467	72.77 217	66.02 430	83.99 417

KD-MVS_2432*1600		66.22 410	63.89 413	73.21 401	75.47 449	53.42 423	70.76 449	84.35 338	64.10 367	66.52 411	78.52 431	34.55 448	84.98 394	50.40 414	50.33 468	81.23 443

miper_refine_blended		66.22 410	63.89 413	73.21 401	75.47 449	53.42 423	70.76 449	84.35 338	64.10 367	66.52 411	78.52 431	34.55 448	84.98 394	50.40 414	50.33 468	81.23 443

tpmvs		71.09 365	69.29 370	76.49 365	82.04 385	56.04 396	78.92 394	81.37 385	64.05 369	67.18 401	78.28 433	49.74 347	89.77 327	49.67 421	72.37 399	83.67 421

our_test_3		69.14 386	67.00 399	75.57 373	79.80 418	58.80 352	77.96 408	77.81 421	59.55 413	62.90 438	78.25 434	47.43 364	83.97 402	51.71 406	67.58 425	83.93 418

MDA-MVSNet_test_wron		65.03 414	62.92 418	71.37 417	75.93 442	56.73 383	69.09 458	74.73 442	57.28 435	54.03 464	77.89 435	45.88 383	74.39 462	49.89 420	61.55 447	82.99 430

YYNet1		65.03 414	62.91 419	71.38 416	75.85 445	56.60 387	69.12 457	74.66 444	57.28 435	54.12 463	77.87 436	45.85 384	74.48 461	49.95 419	61.52 448	83.05 428

ambc				75.24 380	73.16 461	50.51 446	63.05 476	87.47 283		64.28 428	77.81 437	17.80 476	89.73 329	57.88 368	60.64 450	85.49 394

tpm cat1		70.57 371	68.31 377	77.35 358	82.41 382	57.95 364	78.08 406	80.22 402	52.04 451	68.54 384	77.66 438	52.00 313	87.84 363	51.77 405	72.07 404	86.25 379

blended_shiyan6		73.38 335	71.17 351	80.01 299	78.36 431	61.48 320	82.43 339	87.27 289	65.40 349	68.56 383	77.55 439	51.94 316	91.01 303	63.27 309	65.76 432	87.55 346

dp		66.80 404	65.43 405	70.90 424	79.74 420	48.82 452	75.12 431	74.77 441	59.61 412	64.08 431	77.23 440	42.89 406	80.72 426	48.86 426	66.58 428	83.16 426

TESTMET0.1,1		69.89 381	69.00 373	72.55 409	79.27 426	56.85 381	78.38 401	74.71 443	57.64 431	68.09 388	77.19 441	37.75 438	76.70 443	63.92 303	84.09 243	84.10 416

CHOSEN 280x420		66.51 407	64.71 409	71.90 413	81.45 395	63.52 278	57.98 478	68.95 460	53.57 447	62.59 439	76.70 442	46.22 380	75.29 459	55.25 387	79.68 305	76.88 457

PatchT		68.46 394	67.85 385	70.29 425	80.70 405	43.93 469	72.47 441	74.88 440	60.15 408	70.55 356	76.57 443	49.94 344	81.59 419	50.58 412	74.83 379	85.34 397

mvsany_test3		53.99 433	51.45 438	61.61 449	55.51 483	44.74 468	63.52 474	45.41 488	43.69 466	58.11 455	76.45 444	17.99 475	63.76 479	54.77 391	47.59 470	76.34 458

RPMNet		73.51 333	70.49 360	82.58 236	81.32 400	65.19 225	75.92 422	92.27 92	57.60 432	72.73 334	76.45 444	52.30 305	95.43 77	48.14 432	77.71 330	87.11 363

blend_shiyan4		72.29 355	69.65 367	80.21 294	78.24 433	62.16 309	82.29 341	87.27 289	65.41 348	68.43 387	76.42 446	39.91 426	91.23 292	63.21 310	65.66 435	87.22 357

FE-blended-shiyan7		72.94 347	70.66 357	79.79 305	77.80 435	61.03 326	81.31 356	87.15 294	65.18 351	68.09 388	76.28 447	51.32 324	90.97 306	63.06 312	65.76 432	87.35 351

usedtu_blend_shiyan5		73.29 340	70.96 354	80.25 292	77.80 435	62.16 309	84.44 296	87.38 285	64.41 361	68.09 388	76.28 447	51.32 324	91.23 292	63.21 310	65.76 432	87.35 351

dmvs_testset		62.63 421	64.11 412	58.19 452	78.55 429	24.76 490	75.28 427	65.94 467	67.91 314	60.34 446	76.01 449	53.56 294	73.94 465	31.79 470	67.65 424	75.88 459

ADS-MVSNet2		66.20 412	63.33 416	74.82 385	79.92 414	58.75 353	67.55 461	75.19 438	53.37 448	65.25 422	75.86 450	42.32 410	80.53 427	41.57 456	68.91 419	85.18 400

ADS-MVSNet		64.36 417	62.88 420	68.78 433	79.92 414	47.17 457	67.55 461	71.18 452	53.37 448	65.25 422	75.86 450	42.32 410	73.99 464	41.57 456	68.91 419	85.18 400

EGC-MVSNET		52.07 439	47.05 443	67.14 440	83.51 350	60.71 332	80.50 370	67.75 462	0.07 490	0.43 491	75.85 452	24.26 467	81.54 420	28.82 473	62.25 445	59.16 473

new-patchmatchnet		61.73 423	61.73 424	61.70 448	72.74 464	24.50 491	69.16 456	78.03 420	61.40 398	56.72 459	75.53 453	38.42 434	76.48 446	45.95 443	57.67 454	84.13 415

N_pmnet		52.79 437	53.26 435	51.40 462	78.99 427	7.68 496	69.52 453	3.89 495	51.63 454	57.01 458	74.98 454	40.83 421	65.96 477	37.78 463	64.67 438	80.56 449

WB-MVS		54.94 431	54.72 432	55.60 458	73.50 457	20.90 492	74.27 437	61.19 475	59.16 417	50.61 467	74.15 455	47.19 367	75.78 454	17.31 483	35.07 477	70.12 465

patchmatchnet-post											74.00 456	51.12 329	88.60 352

GG-mvs-BLEND				75.38 378	81.59 392	55.80 400	79.32 386	69.63 456		67.19 400	73.67 457	43.24 404	88.90 348	50.41 413	84.50 233	81.45 442

SSC-MVS		53.88 434	53.59 434	54.75 460	72.87 463	19.59 493	73.84 439	60.53 477	57.58 433	49.18 471	73.45 458	46.34 379	75.47 457	16.20 486	32.28 479	69.20 466

Patchmatch-RL test		70.24 376	67.78 389	77.61 353	77.43 438	59.57 348	71.16 446	70.33 453	62.94 382	68.65 381	72.77 459	50.62 334	85.49 389	69.58 255	66.58 428	87.77 340

FPMVS		53.68 435	51.64 437	59.81 451	65.08 475	51.03 442	69.48 454	69.58 457	41.46 468	40.67 475	72.32 460	16.46 478	70.00 472	24.24 479	65.42 436	58.40 475

UnsupCasMVSNet_bld		63.70 419	61.53 425	70.21 426	73.69 456	51.39 440	72.82 440	81.89 377	55.63 442	57.81 456	71.80 461	38.67 433	78.61 433	49.26 424	52.21 466	80.63 447

APD_test1		53.31 436	49.93 441	63.42 447	65.68 474	50.13 447	71.59 445	66.90 465	34.43 477	40.58 476	71.56 462	8.65 487	76.27 448	34.64 468	55.36 460	63.86 471

test_f		52.09 438	50.82 439	55.90 456	53.82 486	42.31 475	59.42 477	58.31 480	36.45 475	56.12 462	70.96 463	12.18 481	57.79 482	53.51 398	56.57 457	67.60 467

PVSNet_0	57.27 20	61.67 424	59.27 427	68.85 432	79.61 421	57.44 375	68.01 459	73.44 447	55.93 441	58.54 453	70.41 464	44.58 395	77.55 439	47.01 436	35.91 476	71.55 464

pmmvs3		57.79 428	54.26 433	68.37 435	64.02 477	56.72 384	75.12 431	65.17 468	40.20 469	52.93 465	69.86 465	20.36 473	75.48 456	45.45 446	55.25 462	72.90 463

test_vis1_rt		60.28 425	58.42 428	65.84 443	67.25 472	55.60 403	70.44 451	60.94 476	44.33 465	59.00 451	66.64 466	24.91 465	68.67 473	62.80 314	69.48 415	73.25 462

new_pmnet		50.91 440	50.29 440	52.78 461	68.58 470	34.94 483	63.71 473	56.63 481	39.73 470	44.95 472	65.47 467	21.93 471	58.48 481	34.98 467	56.62 456	64.92 469

gg-mvs-nofinetune		69.95 380	67.96 383	75.94 368	83.07 362	54.51 415	77.23 415	70.29 454	63.11 378	70.32 360	62.33 468	43.62 402	88.69 350	53.88 396	87.76 176	84.62 410

JIA-IIPM		66.32 409	62.82 421	76.82 363	77.09 440	61.72 317	65.34 469	75.38 437	58.04 429	64.51 427	62.32 469	42.05 414	86.51 376	51.45 409	69.22 418	82.21 436

LCM-MVSNet		54.25 432	49.68 442	67.97 439	53.73 487	45.28 464	66.85 464	80.78 389	35.96 476	39.45 477	62.23 470	8.70 486	78.06 437	48.24 431	51.20 467	80.57 448

PMMVS2		40.82 448	38.86 452	46.69 463	53.84 485	16.45 494	48.61 481	49.92 483	37.49 473	31.67 478	60.97 471	8.14 488	56.42 483	28.42 474	30.72 480	67.19 468

testf1		45.72 443	41.96 447	57.00 453	56.90 481	45.32 462	66.14 466	59.26 478	26.19 481	30.89 480	60.96 472	4.14 490	70.64 470	26.39 477	46.73 472	55.04 476

APD_test2		45.72 443	41.96 447	57.00 453	56.90 481	45.32 462	66.14 466	59.26 478	26.19 481	30.89 480	60.96 472	4.14 490	70.64 470	26.39 477	46.73 472	55.04 476

MVS-HIRNet		59.14 427	57.67 429	63.57 446	81.65 390	43.50 470	71.73 443	65.06 469	39.59 471	51.43 466	57.73 474	38.34 435	82.58 414	39.53 459	73.95 386	64.62 470

ANet_high		50.57 441	46.10 445	63.99 445	48.67 490	39.13 478	70.99 448	80.85 388	61.39 399	31.18 479	57.70 475	17.02 477	73.65 466	31.22 472	15.89 487	79.18 452

PMVS	37.38 22	44.16 447	40.28 451	55.82 457	40.82 492	42.54 474	65.12 470	63.99 472	34.43 477	24.48 483	57.12 476	3.92 492	76.17 450	17.10 484	55.52 459	48.75 478
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 445	45.38 446	45.55 464	73.36 460	26.85 488	67.72 460	34.19 490	54.15 446	49.65 470	56.41 477	25.43 463	62.94 480	19.45 481	28.09 481	46.86 480

test_vis3_rt		49.26 442	47.02 444	56.00 455	54.30 484	45.27 465	66.76 465	48.08 485	36.83 474	44.38 473	53.20 478	7.17 489	64.07 478	56.77 381	55.66 458	58.65 474

test_method		31.52 451	29.28 455	38.23 466	27.03 494	6.50 497	20.94 486	62.21 474	4.05 488	22.35 486	52.50 479	13.33 479	47.58 486	27.04 476	34.04 478	60.62 472

kuosan		39.70 449	40.40 450	37.58 467	64.52 476	26.98 486	65.62 468	33.02 491	46.12 462	42.79 474	48.99 480	24.10 468	46.56 488	12.16 489	26.30 482	39.20 481

DeepMVS_CX				27.40 470	40.17 493	26.90 487		24.59 494	17.44 486	23.95 484	48.61 481	9.77 484	26.48 489	18.06 482	24.47 483	28.83 483

MVE	26.22 23	30.37 453	25.89 457	43.81 465	44.55 491	35.46 482	28.87 485	39.07 489	18.20 485	18.58 487	40.18 482	2.68 493	47.37 487	17.07 485	23.78 484	48.60 479
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 446	41.86 449	55.16 459	77.03 441	51.52 438	32.50 484	80.52 394	32.46 479	27.12 482	35.02 483	9.52 485	75.50 455	22.31 480	60.21 452	38.45 482
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 450	30.64 453	35.15 468	52.87 488	27.67 485	57.09 479	47.86 486	24.64 483	16.40 488	33.05 484	11.23 483	54.90 484	14.46 487	18.15 485	22.87 484

EMVS		30.81 452	29.65 454	34.27 469	50.96 489	25.95 489	56.58 480	46.80 487	24.01 484	15.53 489	30.68 485	12.47 480	54.43 485	12.81 488	17.05 486	22.43 485

tmp_tt		18.61 455	21.40 458	10.23 472	4.82 495	10.11 495	34.70 483	30.74 493	1.48 489	23.91 485	26.07 486	28.42 460	13.41 491	27.12 475	15.35 488	7.17 486

X-MVStestdata		80.37 198	77.83 238	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 487	67.45 128	96.60 37	83.06 87	94.50 57	94.07 77

test_post											5.46 488	50.36 338	84.24 400

test_post1							78.90 395				5.43 489	48.81 361	85.44 391	59.25 352

wuyk23d		16.82 456	15.94 459	19.46 471	58.74 480	31.45 484	39.22 482	3.74 496	6.84 487	6.04 490	2.70 490	1.27 494	24.29 490	10.54 490	14.40 489	2.63 487

testmvs		6.04 459	8.02 462	0.10 474	0.08 496	0.03 499	69.74 452	0.04 497	0.05 491	0.31 492	1.68 491	0.02 496	0.04 492	0.24 491	0.02 490	0.25 489

test123		6.12 458	8.11 461	0.14 473	0.06 497	0.09 498	71.05 447	0.03 498	0.04 492	0.25 493	1.30 492	0.05 495	0.03 493	0.21 492	0.01 491	0.29 488

mmdepth		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

monomultidepth		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

test_blank		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

uanet_test		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

DCPMVS		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

pcd_1.5k_mvsjas		5.26 460	7.02 463	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	63.15 179	0.00 494	0.00 493	0.00 492	0.00 490

sosnet-low-res		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

sosnet		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

uncertanet		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

Regformer		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

uanet		0.00 461	0.00 464	0.00 475	0.00 498	0.00 500	0.00 487	0.00 499	0.00 493	0.00 494	0.00 493	0.00 497	0.00 494	0.00 493	0.00 492	0.00 490

TestfortrainingZip							93.28 12

WAC-MVS						42.58 472								39.46 460

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 152	91.30 18

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 57

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 57

eth-test2					0.00 498

eth-test					0.00 498

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 323	92.39 6				88.94 28	96.63 4	94.85 21

save fliter					93.80 44	72.35 44	90.47 74	91.17 148	74.31 157

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 69

GSMVS																88.96 307

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 324				88.96 307

sam_mvs												50.01 342

MTGPA								92.02 110

MTMP							92.18 39	32.83 492

test9_res														84.90 64	95.70 30	92.87 150

agg_prior2														82.91 91	95.45 33	92.70 155

agg_prior					92.85 68	71.94 52		91.78 126		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 85

旧先验2							86.56 228		58.10 428	87.04 61			88.98 344	74.07 202

新几何2							86.29 242

无先验							87.48 186	88.98 237	60.00 409				94.12 140	67.28 276		88.97 306

原ACMM2							86.86 215

testdata2													91.01 303	62.37 322

segment_acmp												73.08 43

testdata1							84.14 307		75.71 111

test12				86.80 58	92.63 73	70.70 81		91.79 125		82.71 136		71.67 63	96.16 52		94.50 57	93.54 114

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 233

plane_prior5								92.44 82					95.38 82	78.71 143	86.32 201	91.33 210

plane_prior3						68.60 128			78.44 36	78.92 198

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 206

n2								0.00 499

nn								0.00 499

door-mid								69.98 455

test11								92.23 96

door								69.44 458

HQP5-MVS						66.98 183

HQP-NCC					89.33 144		89.17 116		76.41 89	77.23 239

ACMP_Plane					89.33 144		89.17 116		76.41 89	77.23 239

BP-MVS														77.47 158

HQP4-MVS										77.24 238			95.11 94			91.03 220

HQP3-MVS								92.19 104							85.99 210

HQP2-MVS												60.17 236

MDTV_nov1_ep13_2view						37.79 480	75.16 429		55.10 443	66.53 410		49.34 352		53.98 395		87.94 336

ACMMP++_ref															81.95 279

ACMMP++															81.25 284

Test By Simon												64.33 165

High-resolution multi-view benchmark