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	86.62 307	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	82.85 367	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	83.01 362	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	87.92 360	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	82.87 366	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	83.07 361	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	85.91 318	71.11 232	83.18 125	93.48 78	50.54 335	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	86.85 301	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	81.85 378	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		86.37 311			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		84.71 332	59.27 415	85.40 75	92.91 94	62.02 200	89.08 341	68.95 261	91.37 105	86.63 375

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	79.74 306	90.08 116	52.02 429	87.86 178	63.10 472	74.88 142	80.16 180	92.79 100	38.29 435	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	54.69 411	87.89 176	77.44 425	74.88 142	80.27 177	92.79 100	48.96 358	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	53.31 424	87.33 198	77.05 429	75.04 135	80.23 179	92.77 102	48.97 357	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	83.50 406	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		85.48 324	57.43 433	81.80 149	91.98 119	63.28 173	92.27 245	64.60 299	92.99 76	87.27 355

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	61.81 329	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	60.93 326	84.47 292	89.78 194	76.36 95	84.07 104	91.88 122	64.71 162	90.26 317	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	85.27 325	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	83.20 359	54.63 444	79.74 183	91.63 134	58.97 243			91.42 103	86.77 370

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	63.96 371	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	60.67 332	84.77 283	83.90 345	70.65 249	80.00 181	91.20 152	41.08 419	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	49.97 342	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	50.26 417	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	58.34 356	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	86.16 315	74.69 147	80.47 176	91.04 158	62.29 194	90.55 314	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	72.54 409	86.12 285	65.75 210	78.76 395	82.07 375	64.12 365	72.97 331	91.02 161	67.97 122	68.08 474	83.04 89	78.02 327	83.80 419

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	55.97 396	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	90.79 308	82.77 93	87.93 172	93.59 110

Vis-MVSNet (Re-imp)		78.36 248	78.45 220	78.07 342	88.64 178	51.78 435	86.70 222	79.63 407	74.14 163	75.11 298	90.83 166	61.29 216	89.75 327	58.10 365	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	58.75 422	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	86.67 303	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	86.67 303	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	86.94 298	61.87 395	69.52 373	90.61 173	51.71 321	94.53 122	46.38 439	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	85.90 319	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	78.76 325	88.31 190	55.72 400	84.45 295	86.63 306	76.79 76	78.26 214	90.55 175	59.30 241	89.70 329	66.63 282	77.05 338	90.88 226

UniMVSNet_ETH3D		79.10 229	78.24 227	81.70 253	86.85 264	60.24 339	87.28 200	88.79 245	74.25 160	76.84 247	90.53 176	49.48 348	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	63.00 379	77.77 228	90.28 181	66.10 147	95.09 98	61.40 332	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	80.45 370	89.40 210	75.19 131	76.61 256	89.98 187	60.61 230	87.69 364	76.83 169	83.55 255	90.33 251

sd_testset		77.70 268	77.40 253	78.60 328	89.03 161	60.02 341	79.00 391	85.83 320	75.19 131	76.61 256	89.98 187	54.81 278	85.46 389	62.63 318	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	90.79 308	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	46.89 369	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	46.89 369	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	65.03 470	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	78.12 340	88.81 167	67.96 149	65.03 470	88.66 253	70.96 239	79.48 188	89.80 193	58.69 244	74.23 462	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	59.53 348	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	84.55 335	66.03 339	72.38 340	89.64 200	57.56 256	86.04 381	59.61 347	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	45.75 386	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	86.77 302	63.24 375	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	44.17 398	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	49.30 352	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	60.80 329	86.86 215	91.58 136	75.67 114	80.24 178	89.45 210	63.34 172	90.25 318	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	78.54 331	82.50 379	59.85 342	82.18 343	82.84 368	58.96 418	71.15 355	89.41 212	45.48 390	84.77 396	58.82 357	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	84.05 343	71.45 224	76.78 250	89.12 215	49.93 345	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	78.90 323	87.13 253	63.59 273	76.58 417	89.33 213	70.51 251	77.82 224	89.03 218	61.84 201	81.38 421	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	79.37 314	87.13 253	63.59 273	77.12 415	89.33 213	70.51 251	66.22 415	89.03 218	50.36 337	82.78 411	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	86.95 297	70.02 265	75.38 285	88.93 223	51.24 326	92.56 230	75.47 189	89.22 143	93.00 145

baseline1		76.98 282	76.75 270	77.66 350	88.13 198	55.66 401	85.12 274	81.89 376	73.04 196	76.79 249	88.90 224	62.43 192	87.78 363	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	83.70 347	63.98 370	70.20 361	88.89 225	54.01 291	94.80 111	46.66 436	81.88 280	86.01 385

ab-mvs		79.51 214	78.97 211	81.14 270	88.46 184	60.91 327	83.84 311	89.24 225	70.36 256	79.03 195	88.87 226	63.23 177	90.21 319	65.12 294	82.57 272	92.28 176

PEN-MVS		77.73 265	77.69 246	77.84 346	87.07 261	53.91 418	87.91 175	91.18 147	77.56 51	73.14 328	88.82 227	61.23 217	89.17 339	59.95 343	72.37 399	90.43 246

tt0805		78.73 238	77.83 238	81.43 259	85.17 308	60.30 338	89.41 107	90.90 156	71.21 230	77.17 244	88.73 228	46.38 375	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	64.74 356	71.23 353	88.70 229	62.59 188	93.66 165	52.66 401	87.03 190	89.01 303

DTE-MVSNet		76.99 281	76.80 266	77.54 355	86.24 280	53.06 427	87.52 185	90.66 164	77.08 69	72.50 337	88.67 231	60.48 232	89.52 331	57.33 372	70.74 411	90.05 268

PS-CasMVS		78.01 259	78.09 230	77.77 348	87.71 224	54.39 415	88.02 169	91.22 145	77.50 54	73.26 326	88.64 232	60.73 224	88.41 355	61.88 327	73.88 388	90.53 242

cdsmvs_eth3d_5k		19.96 453	26.61 455	0.00 474	0.00 497	0.00 499	0.00 486	89.26 222	0.00 492	0.00 493	88.61 233	61.62 207	0.00 493	0.00 492	0.00 491	0.00 489

lupinMVS		81.39 166	80.27 174	84.76 119	87.35 240	70.21 86	85.55 263	86.41 309	62.85 382	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	64.05 368	66.83 404	88.61 233	46.78 371	92.89 216	57.48 369	78.55 317	87.67 341

mvs_anonymous		79.42 219	79.11 208	80.34 289	84.45 328	57.97 362	82.59 337	87.62 279	67.40 320	76.17 269	88.56 236	68.47 115	89.59 330	70.65 241	86.05 208	93.47 116

CP-MVSNet		78.22 250	78.34 224	77.84 346	87.83 214	54.54 413	87.94 173	91.17 148	77.65 46	73.48 324	88.49 237	62.24 196	88.43 354	62.19 323	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	64.33 363	69.87 370	88.38 240	53.66 293	93.58 166	58.86 356	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	58.35 355	85.06 277	88.61 257	78.56 35	77.65 229	88.34 241	63.81 171	90.66 313	64.98 296	77.22 336	91.80 194

XXY-MVS		75.41 311	75.56 288	74.96 381	83.59 348	57.82 366	80.59 367	83.87 346	66.54 333	74.93 304	88.31 242	63.24 176	80.09 427	62.16 324	76.85 342	86.97 366

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	84.26 341	70.04 264	77.42 233	88.26 245	49.94 343	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	50.91 329	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	50.91 329	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	50.91 329	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	65.01 354	76.16 270	88.13 252	50.56 334	93.03 213	69.68 254	77.56 334	91.11 216

pm-mvs1		77.25 278	76.68 272	78.93 322	84.22 331	58.62 353	86.41 233	88.36 260	71.37 225	73.31 325	88.01 253	61.22 218	89.15 340	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	50.11 340	92.51 234	79.02 137	86.89 193	90.97 223

SD_0403		74.65 319	74.77 303	74.29 390	86.20 282	47.42 454	83.71 314	85.12 327	69.30 284	68.50 385	87.95 255	59.40 240	86.05 380	49.38 421	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	79.24 386	89.12 232	70.76 244	69.79 372	87.86 256	49.09 355	93.20 199	56.21 384	80.16 300	86.65 374
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	74.91 382	90.40 109	45.09 465	80.29 373	78.42 417	78.37 40	76.54 258	87.75 257	44.36 396	87.28 369	57.04 375	83.49 257	92.37 171

WTY-MVS		75.65 306	75.68 285	75.57 372	86.40 278	56.82 381	77.92 409	82.40 371	65.10 351	76.18 267	87.72 258	63.13 182	80.90 424	60.31 341	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	85.05 330	67.63 315	76.75 251	87.70 259	62.25 195	90.82 307	58.53 360	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	60.38 340	86.83 194	86.70 372

COLMAP_ROB	66.92 17	73.01 345	70.41 361	80.81 279	87.13 253	65.63 211	88.30 160	84.19 342	62.96 380	63.80 433	87.69 260	38.04 436	92.56 230	46.66 436	74.91 378	84.24 412
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	59.59 346	85.92 252	86.64 305	66.39 334	66.96 402	87.58 262	39.46 426	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	77.61 352	85.79 291	56.21 394	86.78 219	85.76 321	73.60 177	77.93 223	87.57 263	65.02 159	88.99 342	67.14 279	75.33 372	87.63 342

WR-MVS_H		78.51 245	78.49 219	78.56 330	88.02 204	56.38 390	88.43 151	92.67 72	77.14 65	73.89 318	87.55 265	66.25 144	89.24 337	58.92 355	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	74.25 391	84.28 329	50.85 443	86.41 233	83.45 353	44.56 463	73.23 327	87.54 266	49.38 350	85.70 384	65.90 288	78.44 320	86.19 380

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	67.75 391	87.47 268	41.27 417	93.19 201	58.37 362	75.94 358	87.60 343

TransMVSNet (Re)		75.39 313	74.56 306	77.86 345	85.50 301	57.10 378	86.78 219	86.09 317	72.17 210	71.53 350	87.34 269	63.01 183	89.31 335	56.84 378	61.83 445	87.17 358

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	62.72 314	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	62.72 314	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	62.38 320	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	49.06 356	91.11 296	60.91 336	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				78.22 337	81.77 389	60.57 333		83.30 354	69.25 287	67.54 393	87.20 275	36.33 443	87.28 369	54.34 392	74.62 381	86.80 369

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	79.33 315	89.52 133	56.99 379	85.83 256	83.23 356	73.94 167	76.32 263	87.12 278	51.89 317	91.95 257	48.33 427	83.75 249	89.07 296

thres600view7		76.50 290	75.44 290	79.68 308	89.40 141	57.16 376	85.53 265	83.23 356	73.79 171	76.26 264	87.09 279	51.89 317	91.89 260	48.05 432	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	32.78 450	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	60.21 340	83.37 325	87.78 276	66.11 336	75.37 286	87.06 281	63.27 174	90.48 315	61.38 333	82.43 273	90.40 248

CHOSEN 1792x2688		77.63 271	75.69 284	83.44 190	89.98 122	68.58 129	78.70 396	87.50 282	56.38 438	75.80 274	86.84 282	58.67 246	91.40 286	61.58 331	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		70.96 365	68.09 380	79.58 311	85.15 310	63.62 269	84.58 290	79.83 404	62.31 389	60.32 446	86.73 284	32.02 451	88.96 345	50.28 415	71.57 407	86.15 381

TestCases				79.58 311	85.15 310	63.62 269		79.83 404	62.31 389	60.32 446	86.73 284	32.02 451	88.96 345	50.28 415	71.57 407	86.15 381

LCM-MVSNet-Re		77.05 280	76.94 263	77.36 356	87.20 250	51.60 436	80.06 376	80.46 395	75.20 130	67.69 392	86.72 286	62.48 190	88.98 343	63.44 306	89.25 141	91.51 204

1112_ss		77.40 275	76.43 276	80.32 290	89.11 160	60.41 337	83.65 316	87.72 278	62.13 392	73.05 329	86.72 286	62.58 189	89.97 323	62.11 326	80.80 292	90.59 240

ab-mvs-re		7.23 456	9.64 459	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	86.72 286	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

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	66.78 405	86.70 290	41.95 414	91.51 281	55.64 385	78.14 326	87.17 358
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	79.27 316	89.28 149	58.09 358	81.69 349	87.07 295	59.53 413	72.48 338	86.67 291	61.30 215	89.33 334	60.81 338	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	62.72 314	79.57 306	89.45 289

pmmvs6		74.69 318	73.39 322	78.61 327	81.38 397	57.48 373	86.64 225	87.95 270	64.99 355	70.18 362	86.61 293	50.43 336	89.52 331	62.12 325	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	84.61 334	69.54 279	66.51 412	86.59 294	50.16 339	91.75 265	76.26 175	84.24 241	92.69 157

testgi		66.67 405	66.53 401	67.08 440	75.62 446	41.69 475	75.93 420	76.50 432	66.11 336	65.20 423	86.59 294	35.72 445	74.71 459	43.71 448	73.38 394	84.84 406

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	86.20 314	67.49 318	76.36 262	86.54 298	61.54 208	90.79 308	61.86 328	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	86.40 310	67.55 317	77.81 226	86.48 300	54.10 288	93.15 203	57.75 368	82.72 270	87.20 357

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	79.55 313	89.13 156	57.65 370	85.17 271	83.60 348	73.41 184	76.45 259	86.39 302	52.12 308	91.95 257	48.33 427	83.75 249	89.07 296

thres400		76.50 290	75.37 294	79.86 302	89.13 156	57.65 370	85.17 271	83.60 348	73.41 184	76.45 259	86.39 302	52.12 308	91.95 257	48.33 427	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	74.75 386	79.84 416	57.44 374	83.26 327	85.52 323	62.83 383	79.34 193	86.17 307	45.10 391	79.71 428	78.75 142	81.21 286	87.10 364

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	79.70 307	85.08 313	57.89 364	85.57 259	83.56 350	71.03 237	65.66 417	85.88 311	42.10 412	92.57 229	59.11 353	63.34 440	88.65 320

EPNet_dtu		75.46 309	74.86 301	77.23 359	82.57 378	54.60 412	86.89 213	83.09 360	71.64 217	66.25 414	85.86 312	55.99 271	88.04 359	54.92 389	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	73.51 398	82.80 372	55.01 409	76.12 419	81.69 379	62.47 388	74.68 308	85.85 313	57.32 259	78.11 435	60.86 337	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	73.81 396	75.90 442	59.77 343	80.51 368	82.40 371	58.30 424	81.62 154	85.69 315	44.35 397	76.41 446	76.29 174	78.61 316	85.23 398

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	78.08 341	86.10 287	58.05 359	84.65 288	87.53 281	70.32 259	71.22 354	85.63 318	54.97 277	89.86 324	43.03 451	75.02 377	86.32 377

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	73.23 399	85.30 306	49.01 450	74.58 434	81.57 380	75.21 129	73.68 321	85.58 320	52.53 300	82.05 416	54.33 393	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		72.79 349	70.44 360	79.84 303	88.13 198	65.99 201	85.93 251	84.29 339	65.57 344	67.40 398	85.49 322	46.92 368	92.61 226	35.88 465	74.38 383	80.94 444

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		70.33 374	68.37 375	76.21 366	80.60 406	56.23 393	79.19 388	86.49 308	60.89 400	61.29 441	85.47 323	31.78 453	89.47 333	53.37 398	76.21 356	82.94 430

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	90.77 311	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	65.03 353	67.46 395	85.33 326	53.28 298	91.73 267	58.01 366	83.27 262	81.85 439
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	55.08 443	72.02 345	85.27 327	63.83 170	94.11 141	66.10 286	89.80 133	84.24 412

DIV-MVS_self_test		77.72 266	76.76 268	80.58 284	82.48 381	60.48 335	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	45.50 389	95.12 92	59.11 353	85.83 216	91.11 216

cl____		77.72 266	76.76 268	80.58 284	82.49 380	60.48 335	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	80.36 371	88.64 256	56.29 439	76.45 259	85.17 331	57.64 255	93.28 189	61.34 334	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	82.14 373	59.32 414	69.87 370	85.13 332	52.40 304	88.13 358	60.21 342	74.74 380	84.73 408

TDRefinement		67.49 397	64.34 409	76.92 361	73.47 458	61.07 325	84.86 282	82.98 364	59.77 410	58.30 453	85.13 332	26.06 461	87.89 361	47.92 433	60.59 450	81.81 440

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		59.91 425	57.10 429	68.34 435	67.13 472	46.65 459	74.64 433	67.41 462	48.30 458	62.52 439	85.04 336	20.40 471	75.93 451	42.55 453	45.90 473	82.44 433

test_fmvs1_n		70.86 367	70.24 363	72.73 407	72.51 465	55.28 406	81.27 356	79.71 406	51.49 454	78.73 200	84.87 337	27.54 460	77.02 440	76.06 178	79.97 304	85.88 389

WBMVS		73.43 334	72.81 330	75.28 378	87.91 209	50.99 442	78.59 399	81.31 385	65.51 347	74.47 312	84.83 338	46.39 374	86.68 373	58.41 361	77.86 328	88.17 333

CMPMVS	51.72 21	70.19 376	68.16 378	76.28 365	73.15 461	57.55 372	79.47 383	83.92 344	48.02 459	56.48 459	84.81 339	43.13 404	86.42 377	62.67 317	81.81 281	84.89 405
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 392	67.61 391	71.31 419	78.51 430	47.01 457	84.47 292	84.27 340	42.27 466	66.44 413	84.79 340	40.44 422	83.76 402	58.76 358	68.54 422	83.17 424

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	58.24 364	85.84 215	84.27 411

pmmvs5		71.55 360	70.20 364	75.61 371	77.83 434	56.39 389	81.74 347	80.89 386	57.76 429	67.46 395	84.49 342	49.26 353	85.32 391	57.08 374	75.29 373	85.11 402

reproduce_monomvs		75.40 312	74.38 310	78.46 335	83.92 339	57.80 367	83.78 312	86.94 298	73.47 182	72.25 342	84.47 343	38.74 431	89.27 336	75.32 190	70.53 412	88.31 328

thres200		75.55 307	74.47 308	78.82 324	87.78 218	57.85 365	83.07 333	83.51 351	72.44 205	75.84 273	84.42 344	52.08 311	91.75 265	47.41 434	83.64 254	86.86 368

test_fmvs1		70.93 366	70.52 358	72.16 411	73.71 454	55.05 408	80.82 359	78.77 415	51.21 455	78.58 205	84.41 345	31.20 455	76.94 441	75.88 182	80.12 303	84.47 410

testing3		68.56 391	67.67 390	71.22 420	87.33 245	42.87 470	83.06 334	71.54 450	70.36 256	69.08 378	84.38 346	30.33 457	85.69 385	37.50 463	75.45 368	85.09 403

test_fmvs2		68.35 394	67.48 393	70.98 422	69.50 468	51.95 431	80.05 377	76.38 433	49.33 457	74.65 309	84.38 346	23.30 469	75.40 457	74.51 197	75.17 376	85.60 392

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	73.90 395	88.20 193	47.41 455	78.06 406	79.37 409	74.29 159	73.98 317	84.29 349	44.67 392	83.54 405	51.47 407	87.39 182	90.74 233

testing91		76.54 288	75.66 287	79.18 319	88.43 186	55.89 397	81.08 357	83.00 363	73.76 172	75.34 287	84.29 349	46.20 380	90.07 321	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	79.00 320	88.16 195	55.50 403	80.79 361	81.40 383	73.30 188	75.17 295	84.27 352	44.48 395	90.02 322	64.28 301	84.22 242	91.48 207

UWE-MVS		72.13 357	71.49 343	74.03 393	86.66 272	47.70 452	81.40 355	76.89 431	63.60 374	75.59 276	84.22 353	39.94 424	85.62 386	48.98 424	86.13 207	88.77 315

FE-MVSNET3		76.43 294	75.32 296	79.76 305	83.00 365	60.72 330	81.74 347	88.76 250	68.99 298	72.98 330	84.19 354	56.41 270	90.27 316	62.39 319	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	83.47 352	69.16 291	70.49 358	84.15 356	51.95 314	88.15 357	69.23 257	72.14 403	87.34 352

1314		76.53 289	75.30 297	80.21 294	83.93 338	62.32 306	84.66 286	88.81 244	60.23 406	70.16 364	84.07 357	55.30 276	90.73 312	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	64.22 428	83.85 359	35.10 446	92.56 230	57.44 370	80.83 291	82.16 437

thisisatest0515		77.33 276	75.38 293	83.18 202	85.27 307	63.80 266	82.11 344	83.27 355	65.06 352	75.91 271	83.84 360	49.54 347	94.27 131	67.24 277	86.19 205	91.48 207

test20.03		67.45 398	66.95 399	68.94 429	75.48 447	44.84 466	77.50 411	77.67 421	66.66 327	63.01 435	83.80 361	47.02 367	78.40 433	42.53 454	68.86 421	83.58 421

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	80.71 365	86.13 316	65.70 342	65.46 418	83.74 363	44.60 393	90.91 306	51.13 410	76.89 340	84.74 407

MonoMVSNet		76.49 293	75.80 282	78.58 329	81.55 393	58.45 354	86.36 238	86.22 313	74.87 144	74.73 307	83.73 364	51.79 320	88.73 348	70.78 237	72.15 402	88.55 324

testing11		75.14 315	74.01 313	78.53 332	88.16 195	56.38 390	80.74 364	80.42 397	70.67 245	72.69 336	83.72 365	43.61 402	89.86 324	62.29 322	83.76 248	89.36 292

IterMVS		74.29 321	72.94 329	78.35 336	81.53 394	63.49 279	81.58 350	82.49 370	68.06 313	69.99 367	83.69 366	51.66 322	85.54 387	65.85 289	71.64 406	86.01 385
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 352	71.71 341	74.35 389	82.19 384	52.00 430	79.22 387	77.29 427	64.56 358	72.95 332	83.68 367	51.35 323	83.26 409	58.33 363	75.80 359	87.81 339

UWE-MVS-28		65.32 412	64.93 406	66.49 441	78.70 428	38.55 478	77.86 410	64.39 470	62.00 394	64.13 429	83.60 368	41.44 415	76.00 450	31.39 470	80.89 289	84.92 404

sc_t1		72.19 356	69.51 367	80.23 293	84.81 318	61.09 324	84.68 285	80.22 401	60.70 402	71.27 352	83.58 369	36.59 441	89.24 337	60.41 339	63.31 441	90.37 249

testing222		74.04 326	72.66 332	78.19 338	87.89 210	55.36 404	81.06 358	79.20 412	71.30 228	74.65 309	83.57 370	39.11 430	88.67 350	51.43 409	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	80.65 390	66.81 323	66.88 403	83.42 372	57.86 253	92.19 248	63.47 305	79.57 306	89.91 274

mvs5depth		69.45 383	67.45 394	75.46 376	73.93 452	55.83 398	79.19 388	83.23 356	66.89 322	71.63 349	83.32 373	33.69 449	85.09 392	59.81 345	55.34 460	85.46 394

TinyColmap		67.30 400	64.81 407	74.76 385	81.92 388	56.68 385	80.29 373	81.49 382	60.33 404	56.27 460	83.22 374	24.77 465	87.66 365	45.52 444	69.47 416	79.95 449

mvsany_test1		62.30 421	61.26 425	65.41 443	69.52 467	54.86 410	66.86 462	49.78 483	46.65 460	68.50 385	83.21 375	49.15 354	66.28 475	56.93 377	60.77 448	75.11 459

test_vis1_n		69.85 381	69.21 370	71.77 413	72.66 464	55.27 407	81.48 352	76.21 434	52.03 451	75.30 292	83.20 376	28.97 458	76.22 448	74.60 196	78.41 324	83.81 418

CostFormer		75.24 314	73.90 316	79.27 316	82.65 377	58.27 357	80.80 360	82.73 369	61.57 396	75.33 291	83.13 377	55.52 274	91.07 302	64.98 296	78.34 325	88.45 325

MVStest1		56.63 429	52.76 435	68.25 436	61.67 478	53.25 426	71.67 443	68.90 460	38.59 471	50.59 467	83.05 378	25.08 463	70.66 468	36.76 464	38.56 474	80.83 445

WB-MVSnew		71.96 359	71.65 342	72.89 405	84.67 325	51.88 433	82.29 341	77.57 422	62.31 389	73.67 322	83.00 379	53.49 296	81.10 423	45.75 443	82.13 276	85.70 391

ETVMVS		72.25 355	71.05 352	75.84 368	87.77 220	51.91 432	79.39 384	74.98 438	69.26 286	73.71 320	82.95 380	40.82 421	86.14 379	46.17 440	84.43 238	89.47 288

miper_lstm_enhance		74.11 325	73.11 327	77.13 360	80.11 412	59.62 345	72.23 441	86.92 300	66.76 325	70.40 359	82.92 381	56.93 264	82.92 410	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	86.35 312	72.16 211	74.74 306	82.89 382	46.20 380	92.02 254	68.85 263	81.09 287	91.30 212

K. test v3		71.19 362	68.51 374	79.21 318	83.04 364	57.78 368	84.35 301	76.91 430	72.90 199	62.99 436	82.86 383	39.27 427	91.09 301	61.65 330	52.66 463	88.75 316

MS-PatchMatch		73.83 329	72.67 331	77.30 358	83.87 340	66.02 198	81.82 345	84.66 333	61.37 399	68.61 382	82.82 384	47.29 364	88.21 356	59.27 350	84.32 240	77.68 454

lessismore_v0				78.97 321	81.01 403	57.15 377		65.99 465		61.16 442	82.82 384	39.12 429	91.34 288	59.67 346	46.92 470	88.43 326

D2MVS		74.82 317	73.21 325	79.64 310	79.81 417	62.56 300	80.34 372	87.35 286	64.37 362	68.86 379	82.66 386	46.37 376	90.10 320	67.91 270	81.24 285	86.25 378

Anonymous20231206		68.60 389	67.80 387	71.02 421	80.23 411	50.75 444	78.30 404	80.47 394	56.79 436	66.11 416	82.63 387	46.35 377	78.95 431	43.62 449	75.70 360	83.36 423

MIMVSNet		70.69 369	69.30 368	74.88 383	84.52 326	56.35 392	75.87 423	79.42 408	64.59 357	67.76 390	82.41 388	41.10 418	81.54 419	46.64 438	81.34 283	86.75 371

UBG		73.08 344	72.27 337	75.51 374	88.02 204	51.29 440	78.35 403	77.38 426	65.52 345	73.87 319	82.36 389	45.55 387	86.48 376	55.02 388	84.39 239	88.75 316

OpenMVS_ROB	64.09 19	70.56 371	68.19 377	77.65 351	80.26 409	59.41 349	85.01 278	82.96 365	58.76 421	65.43 419	82.33 390	37.63 438	91.23 292	45.34 446	76.03 357	82.32 434

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		68.00 396	67.69 389	68.90 430	77.55 436	47.43 453	75.70 424	72.95 449	66.66 327	66.56 408	82.29 392	48.06 361	75.87 452	44.97 447	74.51 382	83.41 422

PVSNet	64.34 18	72.08 358	70.87 356	75.69 370	86.21 281	56.44 388	74.37 435	80.73 389	62.06 393	70.17 363	82.23 393	42.86 406	83.31 408	54.77 390	84.45 237	87.32 353

MIMVSNet1		68.58 390	66.78 400	73.98 394	80.07 413	51.82 434	80.77 362	84.37 336	64.40 361	59.75 449	82.16 394	36.47 442	83.63 404	42.73 452	70.33 413	86.48 376

CL-MVSNet_self_test		72.37 352	71.46 344	75.09 380	79.49 423	53.53 420	80.76 363	85.01 331	69.12 292	70.51 357	82.05 395	57.92 252	84.13 400	52.27 403	66.00 431	87.60 343

tpm2		73.26 341	71.46 344	78.63 326	83.34 353	56.71 384	80.65 366	80.40 398	56.63 437	73.55 323	82.02 396	51.80 319	91.24 291	56.35 383	78.42 323	87.95 335

PatchMatch-RL		72.38 351	70.90 355	76.80 363	88.60 179	67.38 171	79.53 382	76.17 435	62.75 385	69.36 375	82.00 397	45.51 388	84.89 395	53.62 396	80.58 295	78.12 453

FE-MVSNET2		72.88 348	71.28 348	77.67 349	78.30 432	57.78 368	84.43 297	88.92 242	69.56 278	64.61 425	81.67 398	46.73 373	88.54 353	59.33 349	67.99 423	86.69 373

FMVSNet5		69.50 382	67.96 382	74.15 392	82.97 369	55.35 405	80.01 378	82.12 374	62.56 387	63.02 434	81.53 399	36.92 439	81.92 417	48.42 426	74.06 385	85.17 401

CR-MVSNet		73.37 336	71.27 349	79.67 309	81.32 400	65.19 225	75.92 421	80.30 399	59.92 409	72.73 334	81.19 400	52.50 302	86.69 372	59.84 344	77.71 330	87.11 362

Patchmtry		70.74 368	69.16 371	75.49 375	80.72 404	54.07 417	74.94 432	80.30 399	58.34 423	70.01 365	81.19 400	52.50 302	86.54 374	53.37 398	71.09 410	85.87 390

IB-MVS	68.01 15	75.85 304	73.36 324	83.31 195	84.76 320	66.03 197	83.38 324	85.06 329	70.21 263	69.40 374	81.05 402	45.76 385	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	60.85 401	72.74 333	81.02 403	47.28 365	93.75 162	67.48 274	85.02 225	89.34 293

LF4IMVS		64.02 417	62.19 421	69.50 427	70.90 466	53.29 425	76.13 418	77.18 428	52.65 449	58.59 451	80.98 404	23.55 468	76.52 444	53.06 400	66.66 427	78.68 452

Anonymous20240521		68.80 388	67.22 397	73.55 397	74.33 450	54.11 416	83.18 328	85.61 322	58.15 425	61.68 440	80.94 405	30.71 456	81.27 422	57.00 376	73.34 395	85.28 397

gm-plane-assit					81.40 396	53.83 419			62.72 386		80.94 405		92.39 239	63.40 307

UnsupCasMVSNet_eth		67.33 399	65.99 403	71.37 416	73.48 457	51.47 438	75.16 428	85.19 326	65.20 350	60.78 443	80.93 407	42.35 408	77.20 439	57.12 373	53.69 462	85.44 395

dmvs_re		71.14 363	70.58 357	72.80 406	81.96 386	59.68 344	75.60 425	79.34 410	68.55 305	69.27 377	80.72 408	49.42 349	76.54 443	52.56 402	77.79 329	82.19 436

MDTV_nov1_ep13			69.97 365		83.18 359	53.48 421	77.10 416	80.18 403	60.45 403	69.33 376	80.44 409	48.89 359	86.90 371	51.60 406	78.51 319

pmmvs-eth3d		70.50 372	67.83 386	78.52 333	77.37 438	66.18 195	81.82 345	81.51 381	58.90 419	63.90 432	80.42 410	42.69 407	86.28 378	58.56 359	65.30 436	83.11 426

tt0320		70.49 373	68.03 381	77.89 344	84.78 319	59.12 350	83.55 320	80.44 396	58.13 426	67.43 397	80.41 411	39.26 428	87.54 366	55.12 387	63.18 442	86.99 365

mmtdpeth		74.16 324	73.01 328	77.60 354	83.72 344	61.13 322	85.10 275	85.10 328	72.06 212	77.21 243	80.33 412	43.84 400	85.75 383	77.14 163	52.61 464	85.91 388

tt0320-xc		70.11 377	67.45 394	78.07 342	85.33 305	59.51 348	83.28 326	78.96 414	58.77 420	67.10 401	80.28 413	36.73 440	87.42 367	56.83 379	59.77 452	87.29 354

PM-MVS		66.41 407	64.14 410	73.20 402	73.92 453	56.45 387	78.97 392	64.96 469	63.88 372	64.72 424	80.24 414	19.84 473	83.44 407	66.24 283	64.52 438	79.71 450

SCA		74.22 323	72.33 336	79.91 301	84.05 336	62.17 308	79.96 379	79.29 411	66.30 335	72.38 340	80.13 415	51.95 314	88.60 351	59.25 351	77.67 333	88.96 307

Patchmatch-test		64.82 415	63.24 416	69.57 426	79.42 424	49.82 448	63.49 474	69.05 458	51.98 452	59.95 448	80.13 415	50.91 329	70.98 467	40.66 457	73.57 390	87.90 337

tpmrst		72.39 350	72.13 338	73.18 403	80.54 407	49.91 447	79.91 380	79.08 413	63.11 377	71.69 348	79.95 417	55.32 275	82.77 412	65.66 291	73.89 387	86.87 367

DSMNet-mixed		57.77 428	56.90 430	60.38 449	67.70 470	35.61 480	69.18 454	53.97 481	32.30 479	57.49 456	79.88 418	40.39 423	68.57 473	38.78 461	72.37 399	76.97 455

MDA-MVSNet-bldmvs		66.68 404	63.66 414	75.75 369	79.28 425	60.56 334	73.92 437	78.35 418	64.43 359	50.13 468	79.87 419	44.02 399	83.67 403	46.10 441	56.86 454	83.03 428

PatchmatchNet		73.12 343	71.33 347	78.49 334	83.18 359	60.85 328	79.63 381	78.57 416	64.13 364	71.73 347	79.81 420	51.20 327	85.97 382	57.40 371	76.36 355	88.66 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVSNET		67.25 401	65.33 405	73.02 404	75.86 443	52.54 428	80.26 375	80.56 392	63.80 373	60.39 444	79.70 421	41.41 416	84.66 398	43.34 450	62.62 443	81.86 438

Syy-MVS		68.05 395	67.85 384	68.67 433	84.68 322	40.97 476	78.62 397	73.08 447	66.65 330	66.74 406	79.46 422	52.11 310	82.30 414	32.89 468	76.38 353	82.75 431

myMVS_eth3d		67.02 402	66.29 402	69.21 428	84.68 322	42.58 471	78.62 397	73.08 447	66.65 330	66.74 406	79.46 422	31.53 454	82.30 414	39.43 460	76.38 353	82.75 431

ppachtmachnet_test		70.04 378	67.34 396	78.14 339	79.80 418	61.13 322	79.19 388	80.59 391	59.16 416	65.27 420	79.29 424	46.75 372	87.29 368	49.33 422	66.72 426	86.00 387

EPMVS		69.02 386	68.16 378	71.59 414	79.61 421	49.80 449	77.40 412	66.93 463	62.82 384	70.01 365	79.05 425	45.79 384	77.86 437	56.58 381	75.26 374	87.13 361

PMMVS		69.34 384	68.67 373	71.35 418	75.67 445	62.03 311	75.17 427	73.46 445	50.00 456	68.68 380	79.05 425	52.07 312	78.13 434	61.16 335	82.77 268	73.90 460

test-LLR		72.94 347	72.43 334	74.48 387	81.35 398	58.04 360	78.38 400	77.46 423	66.66 327	69.95 368	79.00 427	48.06 361	79.24 429	66.13 284	84.83 228	86.15 381

test-mter		71.41 361	70.39 362	74.48 387	81.35 398	58.04 360	78.38 400	77.46 423	60.32 405	69.95 368	79.00 427	36.08 444	79.24 429	66.13 284	84.83 228	86.15 381

KD-MVS_self_test		68.81 387	67.59 392	72.46 410	74.29 451	45.45 460	77.93 408	87.00 296	63.12 376	63.99 431	78.99 429	42.32 409	84.77 396	56.55 382	64.09 439	87.16 360

test_fmvs3		63.36 419	61.82 422	67.98 437	62.51 477	46.96 458	77.37 413	74.03 444	45.24 462	67.50 394	78.79 430	12.16 481	72.98 466	72.77 217	66.02 430	83.99 416

KD-MVS_2432*1600		66.22 409	63.89 412	73.21 400	75.47 448	53.42 422	70.76 448	84.35 337	64.10 366	66.52 410	78.52 431	34.55 447	84.98 393	50.40 413	50.33 467	81.23 442

miper_refine_blended		66.22 409	63.89 412	73.21 400	75.47 448	53.42 422	70.76 448	84.35 337	64.10 366	66.52 410	78.52 431	34.55 447	84.98 393	50.40 413	50.33 467	81.23 442

tpmvs		71.09 364	69.29 369	76.49 364	82.04 385	56.04 395	78.92 393	81.37 384	64.05 368	67.18 400	78.28 433	49.74 346	89.77 326	49.67 420	72.37 399	83.67 420

our_test_3		69.14 385	67.00 398	75.57 372	79.80 418	58.80 351	77.96 407	77.81 420	59.55 412	62.90 437	78.25 434	47.43 363	83.97 401	51.71 405	67.58 425	83.93 417

MDA-MVSNet_test_wron		65.03 413	62.92 417	71.37 416	75.93 441	56.73 382	69.09 457	74.73 441	57.28 434	54.03 463	77.89 435	45.88 382	74.39 461	49.89 419	61.55 446	82.99 429

YYNet1		65.03 413	62.91 418	71.38 415	75.85 444	56.60 386	69.12 456	74.66 443	57.28 434	54.12 462	77.87 436	45.85 383	74.48 460	49.95 418	61.52 447	83.05 427

ambc				75.24 379	73.16 460	50.51 445	63.05 475	87.47 283		64.28 427	77.81 437	17.80 475	89.73 328	57.88 367	60.64 449	85.49 393

tpm cat1		70.57 370	68.31 376	77.35 357	82.41 382	57.95 363	78.08 405	80.22 401	52.04 450	68.54 384	77.66 438	52.00 313	87.84 362	51.77 404	72.07 404	86.25 378

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		66.80 403	65.43 404	70.90 423	79.74 420	48.82 451	75.12 430	74.77 440	59.61 411	64.08 430	77.23 440	42.89 405	80.72 425	48.86 425	66.58 428	83.16 425

TESTMET0.1,1		69.89 380	69.00 372	72.55 408	79.27 426	56.85 380	78.38 400	74.71 442	57.64 430	68.09 388	77.19 441	37.75 437	76.70 442	63.92 303	84.09 243	84.10 415

CHOSEN 280x420		66.51 406	64.71 408	71.90 412	81.45 395	63.52 278	57.98 477	68.95 459	53.57 446	62.59 438	76.70 442	46.22 379	75.29 458	55.25 386	79.68 305	76.88 456

PatchT		68.46 393	67.85 384	70.29 424	80.70 405	43.93 468	72.47 440	74.88 439	60.15 407	70.55 356	76.57 443	49.94 343	81.59 418	50.58 411	74.83 379	85.34 396

mvsany_test3		53.99 432	51.45 437	61.61 448	55.51 482	44.74 467	63.52 473	45.41 487	43.69 465	58.11 454	76.45 444	17.99 474	63.76 478	54.77 390	47.59 469	76.34 457

RPMNet		73.51 333	70.49 359	82.58 236	81.32 400	65.19 225	75.92 421	92.27 92	57.60 431	72.73 334	76.45 444	52.30 305	95.43 77	48.14 431	77.71 330	87.11 362

blend_shiyan4		72.29 354	69.65 366	80.21 294	78.24 433	62.16 309	82.29 341	87.27 289	65.41 348	68.43 387	76.42 446	39.91 425	91.23 292	63.21 310	65.66 434	87.22 356

usedtu_blend_shiyan5		73.29 340	70.96 354	80.25 292	77.80 435	62.16 309	84.44 296	87.38 285	64.41 360	68.09 388	76.28 447	51.32 324	91.23 292	63.21 310	65.76 432	87.35 351

dmvs_testset		62.63 420	64.11 411	58.19 451	78.55 429	24.76 489	75.28 426	65.94 466	67.91 314	60.34 445	76.01 448	53.56 294	73.94 464	31.79 469	67.65 424	75.88 458

ADS-MVSNet2		66.20 411	63.33 415	74.82 384	79.92 414	58.75 352	67.55 460	75.19 437	53.37 447	65.25 421	75.86 449	42.32 409	80.53 426	41.57 455	68.91 419	85.18 399

ADS-MVSNet		64.36 416	62.88 419	68.78 432	79.92 414	47.17 456	67.55 460	71.18 451	53.37 447	65.25 421	75.86 449	42.32 409	73.99 463	41.57 455	68.91 419	85.18 399

EGC-MVSNET		52.07 438	47.05 442	67.14 439	83.51 350	60.71 331	80.50 369	67.75 461	0.07 489	0.43 490	75.85 451	24.26 466	81.54 419	28.82 472	62.25 444	59.16 472

new-patchmatchnet		61.73 422	61.73 423	61.70 447	72.74 463	24.50 490	69.16 455	78.03 419	61.40 397	56.72 458	75.53 452	38.42 433	76.48 445	45.95 442	57.67 453	84.13 414

N_pmnet		52.79 436	53.26 434	51.40 461	78.99 427	7.68 495	69.52 452	3.89 494	51.63 453	57.01 457	74.98 453	40.83 420	65.96 476	37.78 462	64.67 437	80.56 448

WB-MVS		54.94 430	54.72 431	55.60 457	73.50 456	20.90 491	74.27 436	61.19 474	59.16 416	50.61 466	74.15 454	47.19 366	75.78 453	17.31 482	35.07 476	70.12 464

patchmatchnet-post											74.00 455	51.12 328	88.60 351

GG-mvs-BLEND				75.38 377	81.59 392	55.80 399	79.32 385	69.63 455		67.19 399	73.67 456	43.24 403	88.90 347	50.41 412	84.50 233	81.45 441

SSC-MVS		53.88 433	53.59 433	54.75 459	72.87 462	19.59 492	73.84 438	60.53 476	57.58 432	49.18 470	73.45 457	46.34 378	75.47 456	16.20 485	32.28 478	69.20 465

Patchmatch-RL test		70.24 375	67.78 388	77.61 352	77.43 437	59.57 347	71.16 445	70.33 452	62.94 381	68.65 381	72.77 458	50.62 333	85.49 388	69.58 255	66.58 428	87.77 340

FPMVS		53.68 434	51.64 436	59.81 450	65.08 474	51.03 441	69.48 453	69.58 456	41.46 467	40.67 474	72.32 459	16.46 477	70.00 471	24.24 478	65.42 435	58.40 474

UnsupCasMVSNet_bld		63.70 418	61.53 424	70.21 425	73.69 455	51.39 439	72.82 439	81.89 376	55.63 441	57.81 455	71.80 460	38.67 432	78.61 432	49.26 423	52.21 465	80.63 446

APD_test1		53.31 435	49.93 440	63.42 446	65.68 473	50.13 446	71.59 444	66.90 464	34.43 476	40.58 475	71.56 461	8.65 486	76.27 447	34.64 467	55.36 459	63.86 470

test_f		52.09 437	50.82 438	55.90 455	53.82 485	42.31 474	59.42 476	58.31 479	36.45 474	56.12 461	70.96 462	12.18 480	57.79 481	53.51 397	56.57 456	67.60 466

PVSNet_0	57.27 20	61.67 423	59.27 426	68.85 431	79.61 421	57.44 374	68.01 458	73.44 446	55.93 440	58.54 452	70.41 463	44.58 394	77.55 438	47.01 435	35.91 475	71.55 463

pmmvs3		57.79 427	54.26 432	68.37 434	64.02 476	56.72 383	75.12 430	65.17 467	40.20 468	52.93 464	69.86 464	20.36 472	75.48 455	45.45 445	55.25 461	72.90 462

test_vis1_rt		60.28 424	58.42 427	65.84 442	67.25 471	55.60 402	70.44 450	60.94 475	44.33 464	59.00 450	66.64 465	24.91 464	68.67 472	62.80 313	69.48 415	73.25 461

new_pmnet		50.91 439	50.29 439	52.78 460	68.58 469	34.94 482	63.71 472	56.63 480	39.73 469	44.95 471	65.47 466	21.93 470	58.48 480	34.98 466	56.62 455	64.92 468

gg-mvs-nofinetune		69.95 379	67.96 382	75.94 367	83.07 362	54.51 414	77.23 414	70.29 453	63.11 377	70.32 360	62.33 467	43.62 401	88.69 349	53.88 395	87.76 176	84.62 409

JIA-IIPM		66.32 408	62.82 420	76.82 362	77.09 439	61.72 317	65.34 468	75.38 436	58.04 428	64.51 426	62.32 468	42.05 413	86.51 375	51.45 408	69.22 418	82.21 435

LCM-MVSNet		54.25 431	49.68 441	67.97 438	53.73 486	45.28 463	66.85 463	80.78 388	35.96 475	39.45 476	62.23 469	8.70 485	78.06 436	48.24 430	51.20 466	80.57 447

PMMVS2		40.82 447	38.86 451	46.69 462	53.84 484	16.45 493	48.61 480	49.92 482	37.49 472	31.67 477	60.97 470	8.14 487	56.42 482	28.42 473	30.72 479	67.19 467

testf1		45.72 442	41.96 446	57.00 452	56.90 480	45.32 461	66.14 465	59.26 477	26.19 480	30.89 479	60.96 471	4.14 489	70.64 469	26.39 476	46.73 471	55.04 475

APD_test2		45.72 442	41.96 446	57.00 452	56.90 480	45.32 461	66.14 465	59.26 477	26.19 480	30.89 479	60.96 471	4.14 489	70.64 469	26.39 476	46.73 471	55.04 475

MVS-HIRNet		59.14 426	57.67 428	63.57 445	81.65 390	43.50 469	71.73 442	65.06 468	39.59 470	51.43 465	57.73 473	38.34 434	82.58 413	39.53 458	73.95 386	64.62 469

ANet_high		50.57 440	46.10 444	63.99 444	48.67 489	39.13 477	70.99 447	80.85 387	61.39 398	31.18 478	57.70 474	17.02 476	73.65 465	31.22 471	15.89 486	79.18 451

PMVS	37.38 22	44.16 446	40.28 450	55.82 456	40.82 491	42.54 473	65.12 469	63.99 471	34.43 476	24.48 482	57.12 475	3.92 491	76.17 449	17.10 483	55.52 458	48.75 477
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
dongtai		45.42 444	45.38 445	45.55 463	73.36 459	26.85 487	67.72 459	34.19 489	54.15 445	49.65 469	56.41 476	25.43 462	62.94 479	19.45 480	28.09 480	46.86 479

test_vis3_rt		49.26 441	47.02 443	56.00 454	54.30 483	45.27 464	66.76 464	48.08 484	36.83 473	44.38 472	53.20 477	7.17 488	64.07 477	56.77 380	55.66 457	58.65 473

test_method		31.52 450	29.28 454	38.23 465	27.03 493	6.50 496	20.94 485	62.21 473	4.05 487	22.35 485	52.50 478	13.33 478	47.58 485	27.04 475	34.04 477	60.62 471

kuosan		39.70 448	40.40 449	37.58 466	64.52 475	26.98 485	65.62 467	33.02 490	46.12 461	42.79 473	48.99 479	24.10 467	46.56 487	12.16 488	26.30 481	39.20 480

DeepMVS_CX				27.40 469	40.17 492	26.90 486		24.59 493	17.44 485	23.95 483	48.61 480	9.77 483	26.48 488	18.06 481	24.47 482	28.83 482

MVE	26.22 23	30.37 452	25.89 456	43.81 464	44.55 490	35.46 481	28.87 484	39.07 488	18.20 484	18.58 486	40.18 481	2.68 492	47.37 486	17.07 484	23.78 483	48.60 478
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 445	41.86 448	55.16 458	77.03 440	51.52 437	32.50 483	80.52 393	32.46 478	27.12 481	35.02 482	9.52 484	75.50 454	22.31 479	60.21 451	38.45 481
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 449	30.64 452	35.15 467	52.87 487	27.67 484	57.09 478	47.86 485	24.64 482	16.40 487	33.05 483	11.23 482	54.90 483	14.46 486	18.15 484	22.87 483

EMVS		30.81 451	29.65 453	34.27 468	50.96 488	25.95 488	56.58 479	46.80 486	24.01 483	15.53 488	30.68 484	12.47 479	54.43 484	12.81 487	17.05 485	22.43 484

tmp_tt		18.61 454	21.40 457	10.23 471	4.82 494	10.11 494	34.70 482	30.74 492	1.48 488	23.91 484	26.07 485	28.42 459	13.41 490	27.12 474	15.35 487	7.17 485

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	12.47 486	67.45 128	96.60 37	83.06 87	94.50 57	94.07 77

test_post											5.46 487	50.36 337	84.24 399

test_post1							78.90 394				5.43 488	48.81 360	85.44 390	59.25 351

wuyk23d		16.82 455	15.94 458	19.46 470	58.74 479	31.45 483	39.22 481	3.74 495	6.84 486	6.04 489	2.70 489	1.27 493	24.29 489	10.54 489	14.40 488	2.63 486

testmvs		6.04 458	8.02 461	0.10 473	0.08 495	0.03 498	69.74 451	0.04 496	0.05 490	0.31 491	1.68 490	0.02 495	0.04 491	0.24 490	0.02 489	0.25 488

test123		6.12 457	8.11 460	0.14 472	0.06 496	0.09 497	71.05 446	0.03 497	0.04 491	0.25 492	1.30 491	0.05 494	0.03 492	0.21 491	0.01 490	0.29 487

mmdepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

monomultidepth		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

test_blank		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet_test		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

DCPMVS		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

pcd_1.5k_mvsjas		5.26 459	7.02 462	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	63.15 179	0.00 493	0.00 492	0.00 491	0.00 489

sosnet-low-res		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

sosnet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uncertanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

Regformer		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

uanet		0.00 460	0.00 463	0.00 474	0.00 497	0.00 499	0.00 486	0.00 498	0.00 492	0.00 493	0.00 492	0.00 496	0.00 493	0.00 492	0.00 491	0.00 489

TestfortrainingZip							93.28 12

WAC-MVS						42.58 471								39.46 459

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					0.00 497

eth-test					0.00 497

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												50.01 341

MTGPA								92.02 110

MTMP							92.18 39	32.83 491

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		58.10 427	87.04 61			88.98 343	74.07 202

新几何2							86.29 242

无先验							87.48 186	88.98 237	60.00 408				94.12 140	67.28 276		88.97 306

原ACMM2							86.86 215

testdata2													91.01 303	62.37 321

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								0.00 498

nn								0.00 498

door-mid								69.98 454

test11								92.23 96

door								69.44 457

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						37.79 479	75.16 428		55.10 442	66.53 409		49.34 351		53.98 394		87.94 336

ACMMP++_ref															81.95 279

ACMMP++															81.25 284

Test By Simon												64.33 165

High-resolution multi-view benchmark