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.

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_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

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++		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_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 63

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 318	92.39 6				88.94 28	96.63 4	94.85 21

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 141	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
DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 102	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 79
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 121	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 29

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

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

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	93.33 117
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 33

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

PC_three_1452									68.21 306	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

test_part2					95.06 8	72.65 32				91.80 16

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 33
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 147	91.30 18

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 109	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 51
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 228	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

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

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 240	67.30 174	89.50 101	90.98 148	76.25 96	90.56 22	94.75 29	68.38 111	94.24 135	90.80 7	92.32 89	94.19 65

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	93.65 100
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 201	87.08 254	65.21 221	89.09 123	90.21 177	79.67 19	89.98 24	95.02 24	73.17 42	91.71 263	91.30 3	91.60 99	92.34 167

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

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 95

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 97	79.94 17	89.74 27	94.86 26	68.63 108	94.20 136	90.83 5	91.39 104	94.38 55

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 184	87.12 253	66.01 199	88.56 148	89.43 204	75.59 111	89.32 28	94.32 44	72.89 46	91.21 288	90.11 11	92.33 87	93.16 127

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 107	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 132	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 134

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 132	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 134

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 145	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
reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 142	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 127

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 192	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 150
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 163	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 138	86.70 265	65.83 205	88.77 136	89.78 189	75.46 115	88.35 36	93.73 74	69.19 98	93.06 204	91.30 3	88.44 159	94.02 75

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 145	86.26 274	67.40 170	89.18 115	89.31 213	72.50 197	88.31 37	93.86 70	69.66 91	91.96 251	89.81 13	91.05 109	93.38 113

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 280	69.93 92	88.65 144	90.78 157	69.97 263	88.27 38	93.98 66	71.39 67	91.54 273	88.49 35	90.45 120	93.91 80

fmvsm_s_conf0.5_n_2		84.04 94	84.11 95	83.81 175	86.17 278	65.00 229	86.96 206	87.28 281	74.35 150	88.25 39	94.23 50	61.82 198	92.60 222	89.85 12	88.09 164	93.84 86

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 179	87.32 242	65.13 224	88.86 130	91.63 127	75.41 116	88.23 40	93.45 81	68.56 109	92.47 230	89.52 18	92.78 79	93.20 125

ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 95	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 71

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.
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 58

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 142	85.42 297	68.81 116	88.49 150	87.26 283	68.08 307	88.03 44	93.49 77	72.04 57	91.77 259	88.90 29	89.14 146	92.24 174

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

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

fmvsm_s_conf0.1_n_2		83.80 101	83.79 101	83.83 173	85.62 291	64.94 233	87.03 203	86.62 299	74.32 151	87.97 47	94.33 43	60.67 222	92.60 222	89.72 14	87.79 170	93.96 77

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	93.05 136

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 362	69.39 107	89.65 95	90.29 175	73.31 182	87.77 49	94.15 55	71.72 61	93.23 189	90.31 9	90.67 117	93.89 83

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 310	69.51 100	89.62 98	90.58 161	73.42 178	87.75 50	94.02 61	72.85 48	93.24 188	90.37 8	90.75 115	93.96 77

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 233	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

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

MGCFI-Net		85.06 85	85.51 74	83.70 177	89.42 139	63.01 285	89.43 104	92.62 78	76.43 85	87.53 53	91.34 142	72.82 49	93.42 181	81.28 108	88.74 153	94.66 36

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 245	68.54 130	89.57 99	90.44 166	75.31 120	87.49 54	94.39 42	72.86 47	92.72 219	89.04 27	90.56 118	94.16 66

fmvsm_l_conf0.5_n_a		84.13 93	84.16 94	84.06 158	85.38 298	68.40 133	88.34 158	86.85 293	67.48 314	87.48 55	93.40 82	70.89 73	91.61 264	88.38 37	89.22 143	92.16 181

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 130	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 143	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_conf0.1_n_a		83.32 120	82.99 117	84.28 140	83.79 336	68.07 145	89.34 111	82.85 359	69.80 267	87.36 58	94.06 59	68.34 113	91.56 269	87.95 42	83.46 254	93.21 123

fmvsm_s_conf0.5_n_a		83.63 109	83.41 109	84.28 140	86.14 279	68.12 143	89.43 104	82.87 358	70.27 256	87.27 59	93.80 73	69.09 99	91.58 266	88.21 38	83.65 248	93.14 130

fmvsm_s_conf0.1_n		83.56 111	83.38 110	84.10 149	84.86 312	67.28 175	89.40 108	83.01 354	70.67 240	87.08 60	93.96 67	68.38 111	91.45 279	88.56 34	84.50 228	93.56 107

旧先验2							86.56 225		58.10 419	87.04 61			88.98 334	74.07 197

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 404	69.03 110	89.47 102	89.65 196	73.24 186	86.98 62	94.27 47	66.62 132	93.23 189	90.26 10	89.95 130	93.78 92

fmvsm_s_conf0.5_n		83.80 101	83.71 103	84.07 155	86.69 266	67.31 173	89.46 103	83.07 353	71.09 228	86.96 63	93.70 75	69.02 104	91.47 278	88.79 30	84.62 227	93.44 112

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 146	86.84 64	94.65 31	67.31 125	95.77 64	84.80 68	92.85 78	92.84 148

fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 166	87.78 218	66.09 196	89.96 86	90.80 156	77.37 57	86.72 65	94.20 52	72.51 51	92.78 218	89.08 22	92.33 87	93.13 131

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 200	82.14 3	86.65 66	94.28 46	68.28 114	97.46 6	90.81 6	95.31 38	95.15 8

dcpmvs_2		85.63 70	86.15 60	84.06 158	91.71 84	64.94 233	86.47 227	91.87 115	73.63 170	86.60 67	93.02 93	76.57 18	91.87 257	83.36 84	92.15 90	95.35 3

MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 129	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 42
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 174	85.94 69	94.51 35	65.80 148	95.61 67	83.04 89	92.51 83	93.53 110

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 224	92.02 105	79.45 22	85.88 70	94.80 27	68.07 116	96.21 50	86.69 52	95.34 36	93.23 120

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 281	76.41 86	85.80 71	90.22 180	74.15 35	95.37 85	81.82 103	91.88 94	92.65 154

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 70

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 89	73.53 175	85.69 73	94.45 37	65.00 156	95.56 68	82.75 94	91.87 95	92.50 160

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 89	73.53 175	85.69 73	94.45 37	63.87 164		82.75 94	91.87 95	92.50 160

testdata				79.97 292	90.90 98	64.21 252		84.71 324	59.27 407	85.40 75	92.91 94	62.02 195	89.08 332	68.95 256	91.37 105	86.63 367

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 228	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
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 60

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 198	85.22 78	91.90 117	69.47 93	96.42 44	83.28 86	95.94 23	94.35 57

patch_mono-2		83.65 107	84.54 89	80.99 269	90.06 120	65.83 205	84.21 298	88.74 246	71.60 216	85.01 79	92.44 105	74.51 29	83.50 398	82.15 101	92.15 90	93.64 102

TEST9					93.26 56	72.96 25	88.75 138	91.89 113	68.44 303	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	91.89 113	68.69 297	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 138

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 96

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

test_8					93.13 60	72.57 35	88.68 143	91.84 117	68.69 297	84.87 84	93.10 88	74.43 30	95.16 90

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 190	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 54

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 82

h-mvs33		83.15 123	82.19 134	86.02 76	90.56 105	70.85 79	88.15 166	89.16 223	76.02 100	84.67 87	91.39 141	61.54 203	95.50 73	82.71 96	75.48 359	91.72 194

hse-mvs2		81.72 149	80.94 154	84.07 155	88.72 175	67.68 160	85.87 248	87.26 283	76.02 100	84.67 87	88.22 241	61.54 203	93.48 176	82.71 96	73.44 387	91.06 213

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	68.81 105	96.65 34	84.53 72	94.90 45	94.00 76

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 195	84.64 90	91.71 125	71.85 58	96.03 55	84.77 69	94.45 60	94.49 50

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 305	84.61 91	93.48 78	72.32 52	96.15 53	79.00 134	95.43 34	94.28 62

UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 154	82.48 2	84.60 92	93.20 87	69.35 95	95.22 88	71.39 228	90.88 114	93.07 133

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

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 99	96.70 31	84.37 74	94.83 49	94.03 74

agg_prior					92.85 68	71.94 52		91.78 121		84.41 95			94.93 101

NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 99	79.31 24	84.39 96	92.18 109	64.64 158	95.53 71	80.70 116	94.65 52	94.56 46

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 261	79.31 24	84.39 96	92.18 109	64.64 158	95.53 71	80.70 116	90.91 113	93.21 123

VDD-MVS		83.01 128	82.36 130	84.96 107	91.02 95	66.40 191	88.91 128	88.11 257	77.57 49	84.39 96	93.29 85	52.19 301	93.91 152	77.05 159	88.70 154	94.57 44

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 245	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
MSLP-MVS++		85.43 75	85.76 69	84.45 128	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 127	92.94 209	80.36 119	94.35 63	90.16 252

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 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 118	69.04 103	95.43 77	83.93 81	93.77 69	93.01 139

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	85.71 309	69.32 96	95.38 82	80.82 113	91.37 105	92.72 149

VNet		82.21 139	82.41 128	81.62 249	90.82 100	60.93 318	84.47 288	89.78 189	76.36 91	84.07 104	91.88 118	64.71 157	90.26 308	70.68 235	88.89 148	93.66 96

baseline		84.93 86	84.98 83	84.80 117	87.30 243	65.39 218	87.30 196	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

BP-MVS1		84.32 91	83.71 103	86.17 68	87.84 213	67.85 154	89.38 109	89.64 197	77.73 45	83.98 106	92.12 114	56.89 260	95.43 77	84.03 80	91.75 98	95.24 7

test_fmvsmvis_n_1920		84.02 95	83.87 97	84.49 127	84.12 328	69.37 108	88.15 166	87.96 264	70.01 261	83.95 107	93.23 86	68.80 106	91.51 276	88.61 32	89.96 129	92.57 155

PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 112	83.86 108	94.42 40	67.87 120	96.64 35	82.70 98	94.57 56	93.66 96

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	93.62 103
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 99	83.81 110	93.95 68	69.77 90	96.01 58	85.15 62	94.66 51	94.32 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
GDP-MVS		83.52 112	82.64 124	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 196	83.71 111	91.86 120	55.69 267	95.35 86	80.03 122	89.74 134	94.69 32

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	67.93 118	95.95 62	84.20 78	94.39 61	93.23 120

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	67.45 123	96.60 37	83.06 87	94.50 57	94.07 72

X-MVStestdata		80.37 193	77.83 233	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 478	67.45 123	96.60 37	83.06 87	94.50 57	94.07 72

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 263	93.44 32	78.70 34	83.63 115	89.03 213	74.57 27	95.71 66	80.26 121	94.04 67	93.66 96
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
SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 116	91.20 147	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

viewmacassd2359aftdt		83.76 103	83.66 105	84.07 155	86.59 269	64.56 241	86.88 211	91.82 118	75.72 106	83.34 117	92.15 113	68.24 115	92.88 212	79.05 131	89.15 145	94.77 25

E2		84.00 96	83.87 97	84.39 130	87.70 225	64.95 230	86.40 232	92.23 92	75.85 103	83.21 118	91.78 122	70.09 84	93.55 170	79.52 128	88.05 165	94.66 36

E3		84.00 96	83.87 97	84.39 130	87.70 225	64.95 230	86.40 232	92.23 92	75.85 103	83.21 118	91.78 122	70.09 84	93.55 170	79.52 128	88.05 165	94.66 36

LFMVS		81.82 148	81.23 148	83.57 182	91.89 82	63.43 277	89.84 87	81.85 370	77.04 70	83.21 118	93.10 88	52.26 300	93.43 180	71.98 223	89.95 130	93.85 84

VDDNet		81.52 158	80.67 158	84.05 161	90.44 108	64.13 254	89.73 93	85.91 310	71.11 227	83.18 121	93.48 78	50.54 327	93.49 175	73.40 204	88.25 161	94.54 48

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 161	83.16 122	91.07 152	75.94 21	95.19 89	79.94 124	94.38 62	93.55 108

viewmanbaseed2359cas		83.66 106	83.55 106	84.00 166	86.81 261	64.53 242	86.65 221	91.75 123	74.89 136	83.15 123	91.68 126	68.74 107	92.83 216	79.02 132	89.24 142	94.63 39

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

nrg030		83.88 99	83.53 107	84.96 107	86.77 263	69.28 109	90.46 75	92.67 72	74.79 140	82.95 125	91.33 143	72.70 50	93.09 202	80.79 115	79.28 307	92.50 160

EI-MVSNet-Vis-set		84.19 92	83.81 100	85.31 93	88.18 194	67.85 154	87.66 182	89.73 194	80.05 15	82.95 125	89.59 198	70.74 76	94.82 108	80.66 118	84.72 225	93.28 119

MVS_Test		83.15 123	83.06 115	83.41 188	86.86 258	63.21 281	86.11 242	92.00 107	74.31 152	82.87 127	89.44 206	70.03 86	93.21 191	77.39 155	88.50 158	93.81 88

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 200	93.04 46	69.80 267	82.85 128	91.22 146	73.06 44	96.02 57	76.72 168	94.63 54	91.46 204

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	82.82 129	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 100
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 95	76.87 74	82.81 130	94.25 49	66.44 136	96.24 49	82.88 92	94.28 64	93.38 113

test12				86.80 58	92.63 73	70.70 81		91.79 120		82.71 131		71.67 63	96.16 52		94.50 57	93.54 109

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 128	73.89 164	82.67 132	94.09 57	62.60 182	95.54 70	80.93 111	92.93 77	93.57 106

diffmvs_AUTHOR		82.38 137	82.27 133	82.73 227	83.26 350	63.80 261	83.89 305	89.76 191	73.35 181	82.37 133	90.84 160	66.25 139	90.79 300	82.77 93	87.93 168	93.59 105

viewdifsd2359ckpt07		82.83 131	82.78 123	82.99 208	86.51 271	62.58 293	85.09 272	90.83 155	75.22 123	82.28 134	91.63 130	69.43 94	92.03 247	77.71 150	86.32 196	94.34 58

Effi-MVS+		83.62 110	83.08 114	85.24 95	88.38 188	67.45 167	88.89 129	89.15 224	75.50 113	82.27 135	88.28 238	69.61 92	94.45 127	77.81 148	87.84 169	93.84 86

EI-MVSNet-UG-set		83.81 100	83.38 110	85.09 103	87.87 211	67.53 166	87.44 191	89.66 195	79.74 18	82.23 136	89.41 207	70.24 82	94.74 114	79.95 123	83.92 240	92.99 141

KinetiMVS		83.31 121	82.61 125	85.39 91	87.08 254	67.56 165	88.06 168	91.65 126	77.80 44	82.21 137	91.79 121	57.27 255	94.07 142	77.77 149	89.89 132	94.56 46

fmvsm_s_conf0.5_n_7		83.34 118	84.03 96	81.28 260	85.73 288	65.13 224	85.40 264	89.90 187	74.96 134	82.13 138	93.89 69	66.65 131	87.92 352	86.56 53	91.05 109	90.80 223

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 244	90.33 172	76.11 98	82.08 139	91.61 133	71.36 68	94.17 139	81.02 110	92.58 82	92.08 183

diffmvs		82.10 140	81.88 142	82.76 225	83.00 360	63.78 263	83.68 310	89.76 191	72.94 193	82.02 140	89.85 185	65.96 147	90.79 300	82.38 100	87.30 179	93.71 94
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
xiu_mvs_v1_base_debu		80.80 175	79.72 186	84.03 163	87.35 235	70.19 88	85.56 256	88.77 242	69.06 289	81.83 141	88.16 242	50.91 321	92.85 213	78.29 144	87.56 173	89.06 293

xiu_mvs_v1_base		80.80 175	79.72 186	84.03 163	87.35 235	70.19 88	85.56 256	88.77 242	69.06 289	81.83 141	88.16 242	50.91 321	92.85 213	78.29 144	87.56 173	89.06 293

xiu_mvs_v1_base_debi		80.80 175	79.72 186	84.03 163	87.35 235	70.19 88	85.56 256	88.77 242	69.06 289	81.83 141	88.16 242	50.91 321	92.85 213	78.29 144	87.56 173	89.06 293

新几何1				83.42 186	93.13 60	70.71 80		85.48 316	57.43 425	81.80 144	91.98 115	63.28 168	92.27 240	64.60 294	92.99 76	87.27 347

test_yl		81.17 163	80.47 164	83.24 194	89.13 156	63.62 264	86.21 239	89.95 185	72.43 201	81.78 145	89.61 196	57.50 252	93.58 166	70.75 233	86.90 186	92.52 158

DCV-MVSNet		81.17 163	80.47 164	83.24 194	89.13 156	63.62 264	86.21 239	89.95 185	72.43 201	81.78 145	89.61 196	57.50 252	93.58 166	70.75 233	86.90 186	92.52 158

viewdifsd2359ckpt13		82.91 129	82.29 132	84.77 118	86.96 257	66.90 187	87.47 187	91.62 128	72.19 203	81.68 147	90.71 163	66.92 129	93.28 184	75.90 176	87.15 182	94.12 69

viewdifsd2359ckpt09		83.34 118	82.55 126	85.70 81	87.64 229	67.72 159	88.43 151	91.68 125	71.91 210	81.65 148	90.68 164	67.10 128	94.75 113	76.17 171	87.70 172	94.62 41

test_cas_vis1_n_1920		73.76 324	73.74 313	73.81 388	75.90 434	59.77 334	80.51 360	82.40 363	58.30 416	81.62 149	85.69 310	44.35 389	76.41 438	76.29 169	78.61 310	85.23 390

MG-MVS		83.41 115	83.45 108	83.28 191	92.74 71	62.28 302	88.17 164	89.50 202	75.22 123	81.49 150	92.74 103	66.75 130	95.11 94	72.85 210	91.58 101	92.45 164

LuminaMVS		80.68 180	79.62 189	83.83 173	85.07 309	68.01 148	86.99 205	88.83 238	70.36 251	81.38 151	87.99 249	50.11 332	92.51 229	79.02 132	86.89 188	90.97 218

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	81.32 152	91.43 140	70.34 79	97.23 17	84.26 75	93.36 74	94.37 56

MVSFormer		82.85 130	82.05 138	85.24 95	87.35 235	70.21 86	90.50 72	90.38 168	68.55 300	81.32 152	89.47 201	61.68 200	93.46 178	78.98 135	90.26 123	92.05 184

lupinMVS		81.39 161	80.27 169	84.76 119	87.35 235	70.21 86	85.55 259	86.41 301	62.85 374	81.32 152	88.61 228	61.68 200	92.24 242	78.41 142	90.26 123	91.83 187

xiu_mvs_v2_base		81.69 151	81.05 151	83.60 179	89.15 155	68.03 147	84.46 290	90.02 182	70.67 240	81.30 155	86.53 294	63.17 173	94.19 138	75.60 181	88.54 156	88.57 318

PS-MVSNAJ		81.69 151	81.02 152	83.70 177	89.51 134	68.21 142	84.28 297	90.09 181	70.79 237	81.26 156	85.62 314	63.15 174	94.29 129	75.62 180	88.87 149	88.59 317

原ACMM1				84.35 134	93.01 66	68.79 117		92.44 82	63.96 363	81.09 157	91.57 134	66.06 144	95.45 75	67.19 273	94.82 50	88.81 308

jason		81.39 161	80.29 168	84.70 121	86.63 268	69.90 94	85.95 245	86.77 294	63.24 367	81.07 158	89.47 201	61.08 216	92.15 244	78.33 143	90.07 128	92.05 184
jason: jason.
viewmambaseed2359dif		80.41 189	79.84 181	82.12 238	82.95 364	62.50 296	83.39 318	88.06 261	67.11 316	80.98 159	90.31 175	66.20 141	91.01 296	74.62 190	84.90 222	92.86 146

OPM-MVS		83.50 113	82.95 118	85.14 98	88.79 172	70.95 74	89.13 121	91.52 132	77.55 52	80.96 160	91.75 124	60.71 220	94.50 124	79.67 127	86.51 194	89.97 268
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
viewdifsd2359ckpt11		80.37 193	79.73 184	82.30 236	83.70 340	62.39 297	84.20 299	86.67 295	73.22 187	80.90 161	90.62 166	63.00 179	91.56 269	76.81 165	78.44 314	92.95 143

viewmsd2359difaftdt		80.37 193	79.73 184	82.30 236	83.70 340	62.39 297	84.20 299	86.67 295	73.22 187	80.90 161	90.62 166	63.00 179	91.56 269	76.81 165	78.44 314	92.95 143

Vis-MVSNet		83.46 114	82.80 121	85.43 90	90.25 112	68.74 121	90.30 80	90.13 180	76.33 92	80.87 163	92.89 95	61.00 217	94.20 136	72.45 220	90.97 111	93.35 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
AstraMVS		80.81 172	80.14 173	82.80 219	86.05 283	63.96 256	86.46 228	85.90 311	73.71 168	80.85 164	90.56 169	54.06 284	91.57 268	79.72 126	83.97 239	92.86 146

guyue		81.13 165	80.64 159	82.60 230	86.52 270	63.92 259	86.69 220	87.73 272	73.97 160	80.83 165	89.69 192	56.70 261	91.33 284	78.26 147	85.40 218	92.54 157

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	80.73 166	93.82 72	64.33 160	96.29 46	82.67 99	90.69 116	93.23 120
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
SSM_0404		81.91 145	80.84 156	85.13 101	89.24 151	68.26 137	87.84 179	89.25 218	71.06 230	80.62 167	90.39 173	59.57 233	94.65 119	72.45 220	87.19 181	92.47 163

Anonymous20240529		80.19 199	78.89 208	84.10 149	90.60 104	64.75 239	88.95 127	90.90 151	65.97 335	80.59 168	91.17 149	49.97 334	93.73 164	69.16 254	82.70 266	93.81 88

Elysia		81.53 156	80.16 171	85.62 84	85.51 294	68.25 139	88.84 133	92.19 99	71.31 221	80.50 169	89.83 186	46.89 361	94.82 108	76.85 161	89.57 136	93.80 90

StellarMVS		81.53 156	80.16 171	85.62 84	85.51 294	68.25 139	88.84 133	92.19 99	71.31 221	80.50 169	89.83 186	46.89 361	94.82 108	76.85 161	89.57 136	93.80 90

MVS_111021_LR		82.61 134	82.11 135	84.11 148	88.82 166	71.58 57	85.15 269	86.16 307	74.69 142	80.47 171	91.04 153	62.29 189	90.55 306	80.33 120	90.08 127	90.20 251

ECVR-MVS		79.61 206	79.26 199	80.67 277	90.08 116	54.69 403	87.89 176	77.44 417	74.88 137	80.27 172	92.79 100	48.96 350	92.45 231	68.55 260	92.50 84	94.86 19

VPA-MVSNet		80.60 184	80.55 161	80.76 275	88.07 202	60.80 321	86.86 212	91.58 131	75.67 110	80.24 173	89.45 205	63.34 167	90.25 309	70.51 237	79.22 308	91.23 208

test1111		79.43 213	79.18 202	80.15 289	89.99 121	53.31 416	87.33 195	77.05 421	75.04 130	80.23 174	92.77 102	48.97 349	92.33 239	68.87 257	92.40 86	94.81 22

test2506		77.30 272	76.49 269	79.74 297	90.08 116	52.02 421	87.86 178	63.10 464	74.88 137	80.16 175	92.79 100	38.29 427	92.35 237	68.74 259	92.50 84	94.86 19

Anonymous202405211		78.25 244	77.01 255	81.99 243	91.03 94	60.67 323	84.77 279	83.90 337	70.65 244	80.00 176	91.20 147	41.08 411	91.43 280	65.21 288	85.26 219	93.85 84

RRT-MVS		82.60 136	82.10 136	84.10 149	87.98 207	62.94 290	87.45 190	91.27 139	77.42 56	79.85 177	90.28 176	56.62 263	94.70 117	79.87 125	88.15 163	94.67 33

test222					91.50 86	68.26 137	84.16 301	83.20 351	54.63 436	79.74 178	91.63 130	58.97 238			91.42 103	86.77 362

OMC-MVS		82.69 132	81.97 141	84.85 114	88.75 174	67.42 168	87.98 170	90.87 153	74.92 135	79.72 179	91.65 128	62.19 192	93.96 144	75.26 186	86.42 195	93.16 127

FA-MVS(test-final)		80.96 168	79.91 178	84.10 149	88.30 191	65.01 228	84.55 287	90.01 183	73.25 185	79.61 180	87.57 258	58.35 244	94.72 115	71.29 229	86.25 199	92.56 156

CPTT-MVS		83.73 104	83.33 112	84.92 111	93.28 53	70.86 78	92.09 41	90.38 168	68.75 296	79.57 181	92.83 97	60.60 226	93.04 207	80.92 112	91.56 102	90.86 222

IS-MVSNet		83.15 123	82.81 120	84.18 147	89.94 123	63.30 279	91.59 51	88.46 254	79.04 30	79.49 182	92.16 111	65.10 153	94.28 130	67.71 266	91.86 97	94.95 12

mamba_0408		79.37 218	77.52 245	84.93 110	88.81 167	67.96 149	65.03 462	88.66 248	70.96 234	79.48 183	89.80 188	58.69 239	94.65 119	70.35 239	85.93 207	92.18 177

SSM_04072		77.67 265	77.52 245	78.12 331	88.81 167	67.96 149	65.03 462	88.66 248	70.96 234	79.48 183	89.80 188	58.69 239	74.23 454	70.35 239	85.93 207	92.18 177

SSM_0407		81.58 155	80.48 163	84.87 113	88.81 167	67.96 149	87.37 192	89.25 218	71.06 230	79.48 183	90.39 173	59.57 233	94.48 126	72.45 220	85.93 207	92.18 177

PS-MVSNAJss		82.07 142	81.31 146	84.34 135	86.51 271	67.27 176	89.27 112	91.51 133	71.75 211	79.37 186	90.22 180	63.15 174	94.27 131	77.69 151	82.36 269	91.49 201

EPP-MVSNet		83.40 116	83.02 116	84.57 123	90.13 114	64.47 247	92.32 35	90.73 158	74.45 149	79.35 187	91.10 150	69.05 102	95.12 92	72.78 211	87.22 180	94.13 68

test_vis1_n_1920		75.52 302	75.78 278	74.75 378	79.84 410	57.44 366	83.26 322	85.52 315	62.83 375	79.34 188	86.17 302	45.10 383	79.71 420	78.75 137	81.21 281	87.10 355

DP-MVS Recon		83.11 126	82.09 137	86.15 70	94.44 23	70.92 76	88.79 135	92.20 98	70.53 245	79.17 189	91.03 155	64.12 162	96.03 55	68.39 263	90.14 125	91.50 200

ab-mvs		79.51 209	78.97 206	81.14 265	88.46 184	60.91 319	83.84 306	89.24 220	70.36 251	79.03 190	88.87 221	63.23 172	90.21 310	65.12 289	82.57 267	92.28 171

EIA-MVS		83.31 121	82.80 121	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 144	78.96 191	86.42 296	69.06 101	95.26 87	75.54 182	90.09 126	93.62 103

PVSNet_Blended_VisFu		82.62 133	81.83 143	84.96 107	90.80 101	69.76 97	88.74 140	91.70 124	69.39 276	78.96 191	88.46 233	65.47 150	94.87 107	74.42 193	88.57 155	90.24 250

HQP_MVS		83.64 108	83.14 113	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 193	91.00 157	60.42 228	95.38 82	78.71 138	86.32 196	91.33 205

plane_prior3						68.60 128			78.44 36	78.92 193

test_fmvs1_n		70.86 359	70.24 355	72.73 399	72.51 457	55.28 398	81.27 348	79.71 398	51.49 446	78.73 195	84.87 332	27.54 452	77.02 432	76.06 173	79.97 299	85.88 381

EI-MVSNet		80.52 188	79.98 176	82.12 238	84.28 324	63.19 283	86.41 229	88.95 235	74.18 157	78.69 196	87.54 261	66.62 132	92.43 232	72.57 214	80.57 291	90.74 228

MVSTER		79.01 226	77.88 232	82.38 234	83.07 357	64.80 238	84.08 304	88.95 235	69.01 292	78.69 196	87.17 272	54.70 277	92.43 232	74.69 189	80.57 291	89.89 271

API-MVS		81.99 144	81.23 148	84.26 144	90.94 97	70.18 91	91.10 63	89.32 212	71.51 218	78.66 198	88.28 238	65.26 151	95.10 97	64.74 293	91.23 107	87.51 340

GeoE		81.71 150	81.01 153	83.80 176	89.51 134	64.45 248	88.97 126	88.73 247	71.27 224	78.63 199	89.76 191	66.32 138	93.20 194	69.89 246	86.02 204	93.74 93

test_fmvs1		70.93 358	70.52 350	72.16 403	73.71 446	55.05 400	80.82 351	78.77 407	51.21 447	78.58 200	84.41 340	31.20 447	76.94 433	75.88 177	80.12 298	84.47 402

UniMVSNet (Re)		81.60 154	81.11 150	83.09 201	88.38 188	64.41 249	87.60 183	93.02 50	78.42 37	78.56 201	88.16 242	69.78 89	93.26 187	69.58 250	76.49 341	91.60 195

MAR-MVS		81.84 147	80.70 157	85.27 94	91.32 89	71.53 58	89.82 88	90.92 150	69.77 269	78.50 202	86.21 300	62.36 188	94.52 123	65.36 287	92.05 93	89.77 276
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
IMVS_0403		80.80 175	80.12 174	82.87 215	87.13 248	63.59 268	85.19 266	89.33 208	70.51 246	78.49 203	89.03 213	63.26 170	93.27 186	72.56 216	85.56 214	91.74 190

Fast-Effi-MVS+		80.81 172	79.92 177	83.47 183	88.85 163	64.51 244	85.53 261	89.39 206	70.79 237	78.49 203	85.06 329	67.54 122	93.58 166	67.03 276	86.58 192	92.32 169

FIs		82.07 142	82.42 127	81.04 268	88.80 171	58.34 347	88.26 161	93.49 31	76.93 72	78.47 205	91.04 153	69.92 88	92.34 238	69.87 247	84.97 221	92.44 165

UniMVSNet_NR-MVSNet		81.88 146	81.54 145	82.92 212	88.46 184	63.46 275	87.13 199	92.37 86	80.19 12	78.38 206	89.14 209	71.66 64	93.05 205	70.05 243	76.46 342	92.25 172

DU-MVS		81.12 166	80.52 162	82.90 213	87.80 215	63.46 275	87.02 204	91.87 115	79.01 31	78.38 206	89.07 211	65.02 154	93.05 205	70.05 243	76.46 342	92.20 175

CLD-MVS		82.31 138	81.65 144	84.29 139	88.47 183	67.73 158	85.81 252	92.35 87	75.78 105	78.33 208	86.58 291	64.01 163	94.35 128	76.05 174	87.48 176	90.79 224
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VPNet		78.69 235	78.66 211	78.76 316	88.31 190	55.72 392	84.45 291	86.63 298	76.79 76	78.26 209	90.55 170	59.30 236	89.70 320	66.63 277	77.05 332	90.88 221

V42		79.38 217	78.24 222	82.83 216	81.10 396	65.50 215	85.55 259	89.82 188	71.57 217	78.21 210	86.12 303	60.66 223	93.18 197	75.64 179	75.46 361	89.81 275

BH-RMVSNet		79.61 206	78.44 216	83.14 199	89.38 143	65.93 202	84.95 276	87.15 286	73.56 173	78.19 211	89.79 190	56.67 262	93.36 182	59.53 339	86.74 190	90.13 254

v2v482		80.23 197	79.29 198	83.05 205	83.62 342	64.14 253	87.04 202	89.97 184	73.61 171	78.18 212	87.22 269	61.10 215	93.82 156	76.11 172	76.78 338	91.18 209

PVSNet_BlendedMVS		80.60 184	80.02 175	82.36 235	88.85 163	65.40 216	86.16 241	92.00 107	69.34 278	78.11 213	86.09 304	66.02 145	94.27 131	71.52 225	82.06 272	87.39 342

PVSNet_Blended		80.98 167	80.34 166	82.90 213	88.85 163	65.40 216	84.43 292	92.00 107	67.62 311	78.11 213	85.05 330	66.02 145	94.27 131	71.52 225	89.50 138	89.01 298

v1144		80.03 201	79.03 204	83.01 207	83.78 337	64.51 244	87.11 201	90.57 163	71.96 209	78.08 215	86.20 301	61.41 207	93.94 147	74.93 188	77.23 329	90.60 234

FE-MVS		77.78 259	75.68 280	84.08 154	88.09 201	66.00 200	83.13 325	87.79 270	68.42 304	78.01 216	85.23 324	45.50 381	95.12 92	59.11 344	85.83 211	91.11 211

TranMVSNet+NR-MVSNet		80.84 170	80.31 167	82.42 233	87.85 212	62.33 300	87.74 181	91.33 138	80.55 9	77.99 217	89.86 184	65.23 152	92.62 220	67.05 275	75.24 369	92.30 170

Baseline_NR-MVSNet		78.15 249	78.33 220	77.61 344	85.79 286	56.21 386	86.78 216	85.76 313	73.60 172	77.93 218	87.57 258	65.02 154	88.99 333	67.14 274	75.33 366	87.63 336

icg_test_0407_2		78.92 230	78.93 207	78.90 314	87.13 248	63.59 268	76.58 409	89.33 208	70.51 246	77.82 219	89.03 213	61.84 196	81.38 413	72.56 216	85.56 214	91.74 190

IMVS_0407		80.61 182	79.90 179	82.75 226	87.13 248	63.59 268	85.33 265	89.33 208	70.51 246	77.82 219	89.03 213	61.84 196	92.91 210	72.56 216	85.56 214	91.74 190

TR-MVS		77.44 268	76.18 275	81.20 263	88.24 192	63.24 280	84.61 285	86.40 302	67.55 312	77.81 221	86.48 295	54.10 282	93.15 198	57.75 360	82.72 265	87.20 348

v1192		79.59 208	78.43 217	83.07 204	83.55 344	64.52 243	86.93 209	90.58 161	70.83 236	77.78 222	85.90 305	59.15 237	93.94 147	73.96 198	77.19 331	90.76 226

PCF-MVS	73.52 7	80.38 191	78.84 209	85.01 105	87.71 223	68.99 113	83.65 311	91.46 137	63.00 371	77.77 223	90.28 176	66.10 142	95.09 98	61.40 323	88.22 162	90.94 220
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
WR-MVS		79.49 210	79.22 201	80.27 286	88.79 172	58.35 346	85.06 273	88.61 252	78.56 35	77.65 224	88.34 236	63.81 166	90.66 305	64.98 291	77.22 330	91.80 189

XVG-OURS		80.41 189	79.23 200	83.97 169	85.64 290	69.02 112	83.03 330	90.39 167	71.09 228	77.63 225	91.49 138	54.62 279	91.35 282	75.71 178	83.47 253	91.54 198

v144192		79.47 211	78.37 218	82.78 223	83.35 347	63.96 256	86.96 206	90.36 171	69.99 262	77.50 226	85.67 312	60.66 223	93.77 160	74.27 195	76.58 339	90.62 232

v1921920		79.22 220	78.03 226	82.80 219	83.30 349	63.94 258	86.80 214	90.33 172	69.91 265	77.48 227	85.53 316	58.44 243	93.75 162	73.60 200	76.85 336	90.71 230

thisisatest0530		79.40 215	77.76 238	84.31 137	87.69 227	65.10 227	87.36 193	84.26 333	70.04 259	77.42 228	88.26 240	49.94 335	94.79 112	70.20 241	84.70 226	93.03 137

FC-MVSNet-test		81.52 158	82.02 139	80.03 291	88.42 187	55.97 388	87.95 172	93.42 34	77.10 68	77.38 229	90.98 159	69.96 87	91.79 258	68.46 262	84.50 228	92.33 168

v1240		78.99 227	77.78 236	82.64 228	83.21 352	63.54 272	86.62 223	90.30 174	69.74 272	77.33 230	85.68 311	57.04 258	93.76 161	73.13 208	76.92 333	90.62 232

PAPM_NR		83.02 127	82.41 128	84.82 115	92.47 76	66.37 192	87.93 174	91.80 119	73.82 165	77.32 231	90.66 165	67.90 119	94.90 104	70.37 238	89.48 139	93.19 126

ACMM	73.20 8	80.78 179	79.84 181	83.58 181	89.31 147	68.37 134	89.99 84	91.60 130	70.28 255	77.25 232	89.66 194	53.37 291	93.53 173	74.24 196	82.85 262	88.85 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP4-MVS										77.24 233			95.11 94			91.03 215

AUN-MVS		79.21 221	77.60 243	84.05 161	88.71 176	67.61 162	85.84 250	87.26 283	69.08 288	77.23 234	88.14 246	53.20 293	93.47 177	75.50 183	73.45 386	91.06 213

HQP-NCC					89.33 144		89.17 116		76.41 86	77.23 234

ACMP_Plane					89.33 144		89.17 116		76.41 86	77.23 234

HQP-MVS		82.61 134	82.02 139	84.37 132	89.33 144	66.98 183	89.17 116	92.19 99	76.41 86	77.23 234	90.23 179	60.17 231	95.11 94	77.47 153	85.99 205	91.03 215

mmtdpeth		74.16 318	73.01 322	77.60 346	83.72 339	61.13 314	85.10 271	85.10 320	72.06 207	77.21 238	80.33 407	43.84 392	85.75 375	77.14 158	52.61 456	85.91 380

tt0805		78.73 233	77.83 233	81.43 254	85.17 303	60.30 329	89.41 107	90.90 151	71.21 225	77.17 239	88.73 223	46.38 367	93.21 191	72.57 214	78.96 309	90.79 224

TAPA-MVS	73.13 9	79.15 222	77.94 228	82.79 222	89.59 130	62.99 289	88.16 165	91.51 133	65.77 336	77.14 240	91.09 151	60.91 218	93.21 191	50.26 409	87.05 184	92.17 180
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPR		81.66 153	80.89 155	83.99 168	90.27 111	64.00 255	86.76 218	91.77 122	68.84 295	77.13 241	89.50 199	67.63 121	94.88 106	67.55 268	88.52 157	93.09 132

UniMVSNet_ETH3D		79.10 224	78.24 222	81.70 248	86.85 259	60.24 330	87.28 197	88.79 240	74.25 155	76.84 242	90.53 171	49.48 340	91.56 269	67.98 264	82.15 270	93.29 118

EPNet		83.72 105	82.92 119	86.14 72	84.22 326	69.48 101	91.05 64	85.27 317	81.30 6	76.83 243	91.65 128	66.09 143	95.56 68	76.00 175	93.85 68	93.38 113
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline1		76.98 277	76.75 265	77.66 342	88.13 198	55.66 393	85.12 270	81.89 368	73.04 191	76.79 244	88.90 219	62.43 187	87.78 355	63.30 303	71.18 403	89.55 282

tttt0517		79.40 215	77.91 229	83.90 172	88.10 200	63.84 260	88.37 157	84.05 335	71.45 219	76.78 245	89.12 210	49.93 337	94.89 105	70.18 242	83.18 259	92.96 142

TAMVS		78.89 231	77.51 247	83.03 206	87.80 215	67.79 157	84.72 280	85.05 322	67.63 310	76.75 246	87.70 254	62.25 190	90.82 299	58.53 351	87.13 183	90.49 239

XVG-OURS-SEG-HR		80.81 172	79.76 183	83.96 170	85.60 292	68.78 118	83.54 317	90.50 164	70.66 243	76.71 247	91.66 127	60.69 221	91.26 285	76.94 160	81.58 277	91.83 187

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	76.66 248	93.37 83	60.40 230	96.75 30	77.20 156	93.73 70	95.29 6

LPG-MVS_test		82.08 141	81.27 147	84.50 125	89.23 152	68.76 119	90.22 81	91.94 111	75.37 118	76.64 249	91.51 136	54.29 280	94.91 102	78.44 140	83.78 241	89.83 273

LGP-MVS_train				84.50 125	89.23 152	68.76 119		91.94 111	75.37 118	76.64 249	91.51 136	54.29 280	94.91 102	78.44 140	83.78 241	89.83 273

SDMVSNet		80.38 191	80.18 170	80.99 269	89.03 161	64.94 233	80.45 362	89.40 205	75.19 127	76.61 251	89.98 182	60.61 225	87.69 356	76.83 164	83.55 250	90.33 246

sd_testset		77.70 263	77.40 248	78.60 319	89.03 161	60.02 332	79.00 383	85.83 312	75.19 127	76.61 251	89.98 182	54.81 272	85.46 381	62.63 310	83.55 250	90.33 246

testing3-2		75.12 310	75.19 292	74.91 374	90.40 109	45.09 457	80.29 365	78.42 409	78.37 40	76.54 253	87.75 252	44.36 388	87.28 361	57.04 367	83.49 252	92.37 166

tfpn200view9		76.42 289	75.37 289	79.55 304	89.13 156	57.65 362	85.17 267	83.60 340	73.41 179	76.45 254	86.39 297	52.12 302	91.95 252	48.33 419	83.75 244	89.07 291

thres400		76.50 285	75.37 289	79.86 294	89.13 156	57.65 362	85.17 267	83.60 340	73.41 179	76.45 254	86.39 297	52.12 302	91.95 252	48.33 419	83.75 244	90.00 264

HyFIR lowres test		77.53 267	75.40 287	83.94 171	89.59 130	66.62 188	80.36 363	88.64 251	56.29 431	76.45 254	85.17 326	57.64 250	93.28 184	61.34 325	83.10 260	91.91 186

CDS-MVSNet		79.07 225	77.70 240	83.17 198	87.60 230	68.23 141	84.40 295	86.20 306	67.49 313	76.36 257	86.54 293	61.54 203	90.79 300	61.86 319	87.33 178	90.49 239
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres100view900		76.50 285	75.55 284	79.33 306	89.52 133	56.99 371	85.83 251	83.23 348	73.94 162	76.32 258	87.12 273	51.89 310	91.95 252	48.33 419	83.75 244	89.07 291

thres600view7		76.50 285	75.44 285	79.68 299	89.40 141	57.16 368	85.53 261	83.23 348	73.79 166	76.26 259	87.09 274	51.89 310	91.89 255	48.05 424	83.72 247	90.00 264

UGNet		80.83 171	79.59 190	84.54 124	88.04 203	68.09 144	89.42 106	88.16 256	76.95 71	76.22 260	89.46 203	49.30 344	93.94 147	68.48 261	90.31 121	91.60 195
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
test_djsdf		80.30 196	79.32 197	83.27 192	83.98 332	65.37 219	90.50 72	90.38 168	68.55 300	76.19 261	88.70 224	56.44 264	93.46 178	78.98 135	80.14 297	90.97 218

v148		78.72 234	77.80 235	81.47 253	82.73 368	61.96 306	86.30 236	88.08 259	73.26 184	76.18 262	85.47 318	62.46 186	92.36 236	71.92 224	73.82 383	90.09 258

WTY-MVS		75.65 300	75.68 280	75.57 364	86.40 273	56.82 373	77.92 401	82.40 363	65.10 344	76.18 262	87.72 253	63.13 177	80.90 416	60.31 332	81.96 273	89.00 300

mvs_anonymous		79.42 214	79.11 203	80.34 284	84.45 323	57.97 354	82.59 332	87.62 274	67.40 315	76.17 264	88.56 231	68.47 110	89.59 321	70.65 236	86.05 203	93.47 111

Anonymous20231211		78.97 228	77.69 241	82.81 218	90.54 106	64.29 251	90.11 83	91.51 133	65.01 347	76.16 265	88.13 247	50.56 326	93.03 208	69.68 249	77.56 328	91.11 211

thisisatest0515		77.33 271	75.38 288	83.18 197	85.27 302	63.80 261	82.11 337	83.27 347	65.06 345	75.91 266	83.84 354	49.54 339	94.27 131	67.24 272	86.19 200	91.48 202

CANet_DTU		80.61 182	79.87 180	82.83 216	85.60 292	63.17 284	87.36 193	88.65 250	76.37 90	75.88 267	88.44 234	53.51 289	93.07 203	73.30 205	89.74 134	92.25 172

thres200		75.55 301	74.47 302	78.82 315	87.78 218	57.85 357	83.07 328	83.51 343	72.44 200	75.84 268	84.42 339	52.08 305	91.75 260	47.41 426	83.64 249	86.86 360

CHOSEN 1792x2688		77.63 266	75.69 279	83.44 185	89.98 122	68.58 129	78.70 388	87.50 277	56.38 430	75.80 269	86.84 277	58.67 241	91.40 281	61.58 322	85.75 212	90.34 245

AdaColmap		80.58 187	79.42 193	84.06 158	93.09 63	68.91 115	89.36 110	88.97 234	69.27 280	75.70 270	89.69 192	57.20 257	95.77 64	63.06 304	88.41 160	87.50 341

UWE-MVS		72.13 348	71.49 337	74.03 385	86.66 267	47.70 444	81.40 347	76.89 423	63.60 366	75.59 271	84.22 348	39.94 416	85.62 378	48.98 416	86.13 202	88.77 310

c3_l		78.75 232	77.91 229	81.26 261	82.89 365	61.56 311	84.09 303	89.13 226	69.97 263	75.56 272	84.29 344	66.36 137	92.09 246	73.47 203	75.48 359	90.12 255

miper_ehance_all_eth		78.59 238	77.76 238	81.08 267	82.66 370	61.56 311	83.65 311	89.15 224	68.87 294	75.55 273	83.79 356	66.49 135	92.03 247	73.25 206	76.39 344	89.64 279

miper_enhance_ethall		77.87 258	76.86 259	80.92 272	81.65 384	61.38 313	82.68 331	88.98 232	65.52 340	75.47 274	82.30 385	65.76 149	92.00 250	72.95 209	76.39 344	89.39 286

3Dnovator	76.31 5	83.38 117	82.31 131	86.59 61	87.94 208	72.94 28	90.64 68	92.14 104	77.21 63	75.47 274	92.83 97	58.56 242	94.72 115	73.24 207	92.71 81	92.13 182

jajsoiax		79.29 219	77.96 227	83.27 192	84.68 317	66.57 190	89.25 113	90.16 179	69.20 285	75.46 276	89.49 200	45.75 378	93.13 200	76.84 163	80.80 287	90.11 256

IterMVS-LS		80.06 200	79.38 194	82.11 240	85.89 284	63.20 282	86.79 215	89.34 207	74.19 156	75.45 277	86.72 281	66.62 132	92.39 234	72.58 213	76.86 335	90.75 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 211	78.60 212	82.05 241	89.19 154	65.91 203	86.07 243	88.52 253	72.18 204	75.42 278	87.69 255	61.15 214	93.54 172	60.38 331	86.83 189	86.70 364

mvs_tets		79.13 223	77.77 237	83.22 196	84.70 316	66.37 192	89.17 116	90.19 178	69.38 277	75.40 279	89.46 203	44.17 390	93.15 198	76.78 167	80.70 289	90.14 253

mvsmamba		80.60 184	79.38 194	84.27 142	89.74 128	67.24 178	87.47 187	86.95 289	70.02 260	75.38 280	88.93 218	51.24 318	92.56 225	75.47 184	89.22 143	93.00 140

HY-MVS	69.67 12	77.95 255	77.15 253	80.36 283	87.57 234	60.21 331	83.37 320	87.78 271	66.11 331	75.37 281	87.06 276	63.27 169	90.48 307	61.38 324	82.43 268	90.40 243

testing91		76.54 283	75.66 282	79.18 310	88.43 186	55.89 389	81.08 349	83.00 355	73.76 167	75.34 282	84.29 344	46.20 372	90.07 312	64.33 295	84.50 228	91.58 197

GBi-Net		78.40 241	77.40 248	81.40 256	87.60 230	63.01 285	88.39 154	89.28 214	71.63 213	75.34 282	87.28 265	54.80 273	91.11 289	62.72 306	79.57 301	90.09 258

test1		78.40 241	77.40 248	81.40 256	87.60 230	63.01 285	88.39 154	89.28 214	71.63 213	75.34 282	87.28 265	54.80 273	91.11 289	62.72 306	79.57 301	90.09 258

FMVSNet3		77.88 257	76.85 260	80.97 271	86.84 260	62.36 299	86.52 226	88.77 242	71.13 226	75.34 282	86.66 287	54.07 283	91.10 292	62.72 306	79.57 301	89.45 284

CostFormer		75.24 308	73.90 310	79.27 307	82.65 371	58.27 348	80.80 352	82.73 361	61.57 388	75.33 286	83.13 371	55.52 268	91.07 295	64.98 291	78.34 319	88.45 320

test_vis1_n		69.85 373	69.21 362	71.77 405	72.66 456	55.27 399	81.48 344	76.21 426	52.03 443	75.30 287	83.20 370	28.97 450	76.22 440	74.60 191	78.41 318	83.81 410

FMVSNet2		78.20 247	77.21 252	81.20 263	87.60 230	62.89 291	87.47 187	89.02 230	71.63 213	75.29 288	87.28 265	54.80 273	91.10 292	62.38 311	79.38 305	89.61 280

v8		79.97 203	79.02 205	82.80 219	84.09 329	64.50 246	87.96 171	90.29 175	74.13 159	75.24 289	86.81 278	62.88 181	93.89 155	74.39 194	75.40 364	90.00 264

testing99		76.09 295	75.12 294	79.00 311	88.16 195	55.50 395	80.79 353	81.40 375	73.30 183	75.17 290	84.27 347	44.48 387	90.02 313	64.28 296	84.22 237	91.48 202

anonymousdsp		78.60 237	77.15 253	82.98 210	80.51 402	67.08 181	87.24 198	89.53 201	65.66 338	75.16 291	87.19 271	52.52 295	92.25 241	77.17 157	79.34 306	89.61 280

QAPM		80.88 169	79.50 192	85.03 104	88.01 206	68.97 114	91.59 51	92.00 107	66.63 327	75.15 292	92.16 111	57.70 249	95.45 75	63.52 299	88.76 152	90.66 231

v10		79.74 205	78.67 210	82.97 211	84.06 330	64.95 230	87.88 177	90.62 160	73.11 189	75.11 293	86.56 292	61.46 206	94.05 143	73.68 199	75.55 357	89.90 270

Vis-MVSNet (Re-imp)		78.36 243	78.45 215	78.07 333	88.64 178	51.78 427	86.70 219	79.63 399	74.14 158	75.11 293	90.83 161	61.29 211	89.75 318	58.10 356	91.60 99	92.69 152

cl22		78.07 251	77.01 255	81.23 262	82.37 377	61.83 308	83.55 315	87.98 263	68.96 293	75.06 295	83.87 352	61.40 208	91.88 256	73.53 201	76.39 344	89.98 267

ACMP	74.13 6	81.51 160	80.57 160	84.36 133	89.42 139	68.69 126	89.97 85	91.50 136	74.46 148	75.04 296	90.41 172	53.82 286	94.54 121	77.56 152	82.91 261	89.86 272
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
VortexMVS		78.57 239	77.89 231	80.59 278	85.89 284	62.76 292	85.61 253	89.62 198	72.06 207	74.99 297	85.38 320	55.94 266	90.77 303	74.99 187	76.58 339	88.23 324

Effi-MVS+-dtu		80.03 201	78.57 213	84.42 129	85.13 307	68.74 121	88.77 136	88.10 258	74.99 131	74.97 298	83.49 365	57.27 255	93.36 182	73.53 201	80.88 285	91.18 209

XXY-MVS		75.41 305	75.56 283	74.96 373	83.59 343	57.82 358	80.59 359	83.87 338	66.54 328	74.93 299	88.31 237	63.24 171	80.09 419	62.16 315	76.85 336	86.97 358

eth_miper_zixun_eth		77.92 256	76.69 266	81.61 251	83.00 360	61.98 305	83.15 324	89.20 222	69.52 275	74.86 300	84.35 343	61.76 199	92.56 225	71.50 227	72.89 391	90.28 249

GA-MVS		76.87 279	75.17 293	81.97 244	82.75 367	62.58 293	81.44 346	86.35 304	72.16 206	74.74 301	82.89 376	46.20 372	92.02 249	68.85 258	81.09 282	91.30 207

MonoMVSNet		76.49 288	75.80 277	78.58 320	81.55 387	58.45 345	86.36 234	86.22 305	74.87 139	74.73 302	83.73 358	51.79 313	88.73 339	70.78 232	72.15 396	88.55 319

sss		73.60 326	73.64 314	73.51 390	82.80 366	55.01 401	76.12 411	81.69 371	62.47 380	74.68 303	85.85 308	57.32 254	78.11 427	60.86 328	80.93 283	87.39 342

testing222		74.04 320	72.66 326	78.19 329	87.89 210	55.36 396	81.06 350	79.20 404	71.30 223	74.65 304	83.57 364	39.11 422	88.67 341	51.43 401	85.75 212	90.53 237

test_fmvs2		68.35 386	67.48 385	70.98 414	69.50 460	51.95 423	80.05 369	76.38 425	49.33 449	74.65 304	84.38 341	23.30 461	75.40 449	74.51 192	75.17 370	85.60 384

BH-w/o		78.21 246	77.33 251	80.84 273	88.81 167	65.13 224	84.87 277	87.85 269	69.75 270	74.52 306	84.74 336	61.34 209	93.11 201	58.24 355	85.84 210	84.27 403

WBMVS		73.43 328	72.81 324	75.28 370	87.91 209	50.99 434	78.59 391	81.31 377	65.51 342	74.47 307	84.83 333	46.39 366	86.68 365	58.41 352	77.86 322	88.17 327

FMVSNet1		77.44 268	76.12 276	81.40 256	86.81 261	63.01 285	88.39 154	89.28 214	70.49 250	74.39 308	87.28 265	49.06 348	91.11 289	60.91 327	78.52 312	90.09 258

cl____		77.72 261	76.76 263	80.58 279	82.49 374	60.48 326	83.09 326	87.87 267	69.22 283	74.38 309	85.22 325	62.10 193	91.53 274	71.09 230	75.41 363	89.73 278

DIV-MVS_self_test		77.72 261	76.76 263	80.58 279	82.48 375	60.48 326	83.09 326	87.86 268	69.22 283	74.38 309	85.24 323	62.10 193	91.53 274	71.09 230	75.40 364	89.74 277

114514_t		80.68 180	79.51 191	84.20 146	94.09 42	67.27 176	89.64 96	91.11 146	58.75 414	74.08 311	90.72 162	58.10 245	95.04 99	69.70 248	89.42 140	90.30 248

myMVS_eth3d28		73.62 325	73.53 315	73.90 387	88.20 193	47.41 447	78.06 398	79.37 401	74.29 154	73.98 312	84.29 344	44.67 384	83.54 397	51.47 399	87.39 177	90.74 228

WR-MVS_H		78.51 240	78.49 214	78.56 321	88.02 204	56.38 382	88.43 151	92.67 72	77.14 65	73.89 313	87.55 260	66.25 139	89.24 328	58.92 346	73.55 385	90.06 262

UBG		73.08 336	72.27 331	75.51 366	88.02 204	51.29 432	78.35 395	77.38 418	65.52 340	73.87 314	82.36 383	45.55 379	86.48 368	55.02 380	84.39 234	88.75 311

ETVMVS		72.25 346	71.05 345	75.84 360	87.77 220	51.91 424	79.39 376	74.98 430	69.26 281	73.71 315	82.95 374	40.82 413	86.14 371	46.17 432	84.43 233	89.47 283

SSC-MVS3.2		73.35 332	73.39 316	73.23 391	85.30 301	49.01 442	74.58 426	81.57 372	75.21 125	73.68 316	85.58 315	52.53 294	82.05 408	54.33 385	77.69 326	88.63 316

WB-MVSnew		71.96 351	71.65 336	72.89 397	84.67 320	51.88 425	82.29 335	77.57 414	62.31 381	73.67 317	83.00 373	53.49 290	81.10 415	45.75 435	82.13 271	85.70 383

tpm2		73.26 333	71.46 338	78.63 317	83.34 348	56.71 376	80.65 358	80.40 390	56.63 429	73.55 318	82.02 390	51.80 312	91.24 286	56.35 375	78.42 317	87.95 329

CP-MVSNet		78.22 245	78.34 219	77.84 338	87.83 214	54.54 405	87.94 173	91.17 143	77.65 46	73.48 319	88.49 232	62.24 191	88.43 346	62.19 314	74.07 378	90.55 236

pm-mvs1		77.25 273	76.68 267	78.93 313	84.22 326	58.62 344	86.41 229	88.36 255	71.37 220	73.31 320	88.01 248	61.22 213	89.15 331	64.24 297	73.01 390	89.03 297

PS-CasMVS		78.01 254	78.09 225	77.77 340	87.71 223	54.39 407	88.02 169	91.22 140	77.50 54	73.26 321	88.64 227	60.73 219	88.41 347	61.88 318	73.88 382	90.53 237

CVMVSNet		72.99 338	72.58 327	74.25 383	84.28 324	50.85 435	86.41 229	83.45 345	44.56 455	73.23 322	87.54 261	49.38 342	85.70 376	65.90 283	78.44 314	86.19 372

PEN-MVS		77.73 260	77.69 241	77.84 338	87.07 256	53.91 410	87.91 175	91.18 142	77.56 51	73.14 323	88.82 222	61.23 212	89.17 330	59.95 334	72.37 393	90.43 241

1112_ss		77.40 270	76.43 271	80.32 285	89.11 160	60.41 328	83.65 311	87.72 273	62.13 384	73.05 324	86.72 281	62.58 184	89.97 314	62.11 317	80.80 287	90.59 235

mamv4		76.81 280	78.23 224	72.54 401	86.12 280	65.75 210	78.76 387	82.07 367	64.12 357	72.97 325	91.02 156	67.97 117	68.08 466	83.04 89	78.02 321	83.80 411

tpm		72.37 344	71.71 335	74.35 381	82.19 378	52.00 422	79.22 379	77.29 419	64.56 351	72.95 326	83.68 361	51.35 316	83.26 401	58.33 354	75.80 353	87.81 333

cascas		76.72 282	74.64 298	82.99 208	85.78 287	65.88 204	82.33 334	89.21 221	60.85 393	72.74 327	81.02 398	47.28 357	93.75 162	67.48 269	85.02 220	89.34 288

CR-MVSNet		73.37 329	71.27 343	79.67 300	81.32 394	65.19 222	75.92 413	80.30 391	59.92 401	72.73 328	81.19 395	52.50 296	86.69 364	59.84 335	77.71 324	87.11 353

RPMNet		73.51 327	70.49 351	82.58 231	81.32 394	65.19 222	75.92 413	92.27 89	57.60 423	72.73 328	76.45 438	52.30 299	95.43 77	48.14 423	77.71 324	87.11 353

testing11		75.14 309	74.01 307	78.53 323	88.16 195	56.38 382	80.74 356	80.42 389	70.67 240	72.69 330	83.72 359	43.61 394	89.86 315	62.29 313	83.76 243	89.36 287

DTE-MVSNet		76.99 276	76.80 261	77.54 347	86.24 275	53.06 419	87.52 185	90.66 159	77.08 69	72.50 331	88.67 226	60.48 227	89.52 322	57.33 364	70.74 405	90.05 263

Test_1112_low_res		76.40 290	75.44 285	79.27 307	89.28 149	58.09 350	81.69 341	87.07 287	59.53 405	72.48 332	86.67 286	61.30 210	89.33 325	60.81 329	80.15 296	90.41 242

v7n		78.97 228	77.58 244	83.14 199	83.45 346	65.51 214	88.32 159	91.21 141	73.69 169	72.41 333	86.32 299	57.93 246	93.81 157	69.18 253	75.65 355	90.11 256

SCA		74.22 317	72.33 330	79.91 293	84.05 331	62.17 303	79.96 371	79.29 403	66.30 330	72.38 334	80.13 410	51.95 308	88.60 343	59.25 342	77.67 327	88.96 302

CNLPA		78.08 250	76.79 262	81.97 244	90.40 109	71.07 70	87.59 184	84.55 327	66.03 334	72.38 334	89.64 195	57.56 251	86.04 373	59.61 338	83.35 255	88.79 309

reproduce_monomvs		75.40 306	74.38 304	78.46 326	83.92 334	57.80 359	83.78 307	86.94 290	73.47 177	72.25 336	84.47 338	38.74 423	89.27 327	75.32 185	70.53 406	88.31 323

NR-MVSNet		80.23 197	79.38 194	82.78 223	87.80 215	63.34 278	86.31 235	91.09 147	79.01 31	72.17 337	89.07 211	67.20 126	92.81 217	66.08 282	75.65 355	92.20 175

OpenMVS	72.83 10	79.77 204	78.33 220	84.09 153	85.17 303	69.91 93	90.57 69	90.97 149	66.70 321	72.17 337	91.91 116	54.70 277	93.96 144	61.81 320	90.95 112	88.41 322

MVS		78.19 248	76.99 257	81.78 246	85.66 289	66.99 182	84.66 282	90.47 165	55.08 435	72.02 339	85.27 322	63.83 165	94.11 141	66.10 281	89.80 133	84.24 404

XVG-ACMP-BASELINE		76.11 294	74.27 306	81.62 249	83.20 353	64.67 240	83.60 314	89.75 193	69.75 270	71.85 340	87.09 274	32.78 442	92.11 245	69.99 245	80.43 293	88.09 328

PatchmatchNet		73.12 335	71.33 341	78.49 325	83.18 354	60.85 320	79.63 373	78.57 408	64.13 356	71.73 341	79.81 415	51.20 319	85.97 374	57.40 363	76.36 349	88.66 314
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		72.39 342	72.13 332	73.18 395	80.54 401	49.91 439	79.91 372	79.08 405	63.11 369	71.69 342	79.95 412	55.32 269	82.77 404	65.66 286	73.89 381	86.87 359

mvs5depth		69.45 375	67.45 386	75.46 368	73.93 444	55.83 390	79.19 380	83.23 348	66.89 317	71.63 343	83.32 367	33.69 441	85.09 384	59.81 336	55.34 452	85.46 386

TransMVSNet (Re)		75.39 307	74.56 300	77.86 337	85.50 296	57.10 370	86.78 216	86.09 309	72.17 205	71.53 344	87.34 264	63.01 178	89.31 326	56.84 370	61.83 437	87.17 349

Fast-Effi-MVS+-dtu		78.02 253	76.49 269	82.62 229	83.16 356	66.96 185	86.94 208	87.45 279	72.45 198	71.49 345	84.17 349	54.79 276	91.58 266	67.61 267	80.31 294	89.30 289

sc_t1		72.19 347	69.51 359	80.23 287	84.81 313	61.09 316	84.68 281	80.22 393	60.70 394	71.27 346	83.58 363	36.59 433	89.24 328	60.41 330	63.31 433	90.37 244

PAPM		77.68 264	76.40 273	81.51 252	87.29 244	61.85 307	83.78 307	89.59 199	64.74 349	71.23 347	88.70 224	62.59 183	93.66 165	52.66 393	87.03 185	89.01 298

tfpnnormal		74.39 314	73.16 320	78.08 332	86.10 282	58.05 351	84.65 284	87.53 276	70.32 254	71.22 348	85.63 313	54.97 271	89.86 315	43.03 443	75.02 371	86.32 369

RPSCF		73.23 334	71.46 338	78.54 322	82.50 373	59.85 333	82.18 336	82.84 360	58.96 410	71.15 349	89.41 207	45.48 382	84.77 388	58.82 348	71.83 399	91.02 217

PatchT		68.46 385	67.85 376	70.29 416	80.70 399	43.93 460	72.47 432	74.88 431	60.15 399	70.55 350	76.57 437	49.94 335	81.59 410	50.58 403	74.83 373	85.34 388

CL-MVSNet_self_test		72.37 344	71.46 338	75.09 372	79.49 417	53.53 412	80.76 355	85.01 323	69.12 287	70.51 351	82.05 389	57.92 247	84.13 392	52.27 395	66.00 426	87.60 337

IterMVS-SCA-FT		75.43 304	73.87 311	80.11 290	82.69 369	64.85 237	81.57 343	83.47 344	69.16 286	70.49 352	84.15 350	51.95 308	88.15 349	69.23 252	72.14 397	87.34 344

miper_lstm_enhance		74.11 319	73.11 321	77.13 352	80.11 406	59.62 336	72.23 433	86.92 292	66.76 320	70.40 353	82.92 375	56.93 259	82.92 402	69.06 255	72.63 392	88.87 305

gg-mvs-nofinetune		69.95 371	67.96 374	75.94 359	83.07 357	54.51 406	77.23 406	70.29 445	63.11 369	70.32 354	62.33 459	43.62 393	88.69 340	53.88 387	87.76 171	84.62 401

DP-MVS		76.78 281	74.57 299	83.42 186	93.29 52	69.46 104	88.55 149	83.70 339	63.98 362	70.20 355	88.89 220	54.01 285	94.80 111	46.66 428	81.88 275	86.01 377

pmmvs6		74.69 312	73.39 316	78.61 318	81.38 391	57.48 365	86.64 222	87.95 265	64.99 348	70.18 356	86.61 288	50.43 328	89.52 322	62.12 316	70.18 408	88.83 307

PVSNet	64.34 18	72.08 349	70.87 348	75.69 362	86.21 276	56.44 380	74.37 427	80.73 381	62.06 385	70.17 357	82.23 387	42.86 398	83.31 400	54.77 382	84.45 232	87.32 345

1314		76.53 284	75.30 291	80.21 288	83.93 333	62.32 301	84.66 282	88.81 239	60.23 398	70.16 358	84.07 351	55.30 270	90.73 304	67.37 270	83.21 258	87.59 339

Patchmtry		70.74 360	69.16 363	75.49 367	80.72 398	54.07 409	74.94 424	80.30 391	58.34 415	70.01 359	81.19 395	52.50 296	86.54 366	53.37 390	71.09 404	85.87 382

EPMVS		69.02 378	68.16 370	71.59 406	79.61 415	49.80 441	77.40 404	66.93 455	62.82 376	70.01 359	79.05 420	45.79 376	77.86 429	56.58 373	75.26 368	87.13 352

IterMVS		74.29 315	72.94 323	78.35 327	81.53 388	63.49 274	81.58 342	82.49 362	68.06 308	69.99 361	83.69 360	51.66 315	85.54 379	65.85 284	71.64 400	86.01 377
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test-LLR		72.94 339	72.43 328	74.48 379	81.35 392	58.04 352	78.38 392	77.46 415	66.66 322	69.95 362	79.00 422	48.06 353	79.24 421	66.13 279	84.83 223	86.15 373

test-mter		71.41 353	70.39 354	74.48 379	81.35 392	58.04 352	78.38 392	77.46 415	60.32 397	69.95 362	79.00 422	36.08 436	79.24 421	66.13 279	84.83 223	86.15 373

pmmvs4		74.03 322	71.91 333	80.39 282	81.96 380	68.32 135	81.45 345	82.14 365	59.32 406	69.87 364	85.13 327	52.40 298	88.13 350	60.21 333	74.74 374	84.73 400

PLC	70.83 11	78.05 252	76.37 274	83.08 203	91.88 83	67.80 156	88.19 163	89.46 203	64.33 355	69.87 364	88.38 235	53.66 287	93.58 166	58.86 347	82.73 264	87.86 332
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LTVRE_ROB	69.57 13	76.25 292	74.54 301	81.41 255	88.60 179	64.38 250	79.24 378	89.12 227	70.76 239	69.79 366	87.86 251	49.09 347	93.20 194	56.21 376	80.16 295	86.65 366
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
LS3D		76.95 278	74.82 296	83.37 189	90.45 107	67.36 172	89.15 120	86.94 290	61.87 387	69.52 367	90.61 168	51.71 314	94.53 122	46.38 431	86.71 191	88.21 326

IB-MVS	68.01 15	75.85 298	73.36 318	83.31 190	84.76 315	66.03 197	83.38 319	85.06 321	70.21 258	69.40 368	81.05 397	45.76 377	94.66 118	65.10 290	75.49 358	89.25 290
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
PatchMatch-RL		72.38 343	70.90 347	76.80 355	88.60 179	67.38 171	79.53 374	76.17 427	62.75 377	69.36 369	82.00 391	45.51 380	84.89 387	53.62 388	80.58 290	78.12 445

MDTV_nov1_ep13			69.97 358		83.18 354	53.48 413	77.10 408	80.18 395	60.45 395	69.33 370	80.44 404	48.89 351	86.90 363	51.60 398	78.51 313

dmvs_re		71.14 355	70.58 349	72.80 398	81.96 380	59.68 335	75.60 417	79.34 402	68.55 300	69.27 371	80.72 403	49.42 341	76.54 435	52.56 394	77.79 323	82.19 428

testing3		68.56 383	67.67 382	71.22 412	87.33 240	42.87 462	83.06 329	71.54 442	70.36 251	69.08 372	84.38 341	30.33 449	85.69 377	37.50 455	75.45 362	85.09 395

D2MVS		74.82 311	73.21 319	79.64 301	79.81 411	62.56 295	80.34 364	87.35 280	64.37 354	68.86 373	82.66 380	46.37 368	90.10 311	67.91 265	81.24 280	86.25 370

PMMVS		69.34 376	68.67 365	71.35 410	75.67 437	62.03 304	75.17 419	73.46 437	50.00 448	68.68 374	79.05 420	52.07 306	78.13 426	61.16 326	82.77 263	73.90 452

Patchmatch-RL test		70.24 367	67.78 380	77.61 344	77.43 428	59.57 338	71.16 437	70.33 444	62.94 373	68.65 375	72.77 450	50.62 325	85.49 380	69.58 250	66.58 423	87.77 334

MS-PatchMatch		73.83 323	72.67 325	77.30 350	83.87 335	66.02 198	81.82 338	84.66 325	61.37 391	68.61 376	82.82 378	47.29 356	88.21 348	59.27 341	84.32 235	77.68 446

tpm cat1		70.57 362	68.31 368	77.35 349	82.41 376	57.95 355	78.08 397	80.22 393	52.04 442	68.54 377	77.66 433	52.00 307	87.84 354	51.77 396	72.07 398	86.25 370

SD_0403		74.65 313	74.77 297	74.29 382	86.20 277	47.42 446	83.71 309	85.12 319	69.30 279	68.50 378	87.95 250	59.40 235	86.05 372	49.38 413	83.35 255	89.40 285

mvsany_test1		62.30 413	61.26 417	65.41 435	69.52 459	54.86 402	66.86 454	49.78 475	46.65 452	68.50 378	83.21 369	49.15 346	66.28 467	56.93 369	60.77 440	75.11 451

TESTMET0.1,1		69.89 372	69.00 364	72.55 400	79.27 420	56.85 372	78.38 392	74.71 434	57.64 422	68.09 380	77.19 435	37.75 429	76.70 434	63.92 298	84.09 238	84.10 407

MIMVSNet		70.69 361	69.30 360	74.88 375	84.52 321	56.35 384	75.87 415	79.42 400	64.59 350	67.76 381	82.41 382	41.10 410	81.54 411	46.64 430	81.34 278	86.75 363

ACMH+	68.96 14	76.01 296	74.01 307	82.03 242	88.60 179	65.31 220	88.86 130	87.55 275	70.25 257	67.75 382	87.47 263	41.27 409	93.19 196	58.37 353	75.94 352	87.60 337

LCM-MVSNet-Re		77.05 275	76.94 258	77.36 348	87.20 245	51.60 428	80.06 368	80.46 387	75.20 126	67.69 383	86.72 281	62.48 185	88.98 334	63.44 301	89.25 141	91.51 199

ITE_SJBPF				78.22 328	81.77 383	60.57 324		83.30 346	69.25 282	67.54 384	87.20 270	36.33 435	87.28 361	54.34 384	74.62 375	86.80 361

test_fmvs3		63.36 411	61.82 414	67.98 429	62.51 469	46.96 450	77.37 405	74.03 436	45.24 454	67.50 385	78.79 425	12.16 473	72.98 458	72.77 212	66.02 425	83.99 408

pmmvs5		71.55 352	70.20 356	75.61 363	77.83 426	56.39 381	81.74 340	80.89 378	57.76 421	67.46 386	84.49 337	49.26 345	85.32 383	57.08 366	75.29 367	85.11 394

MVP-Stereo		76.12 293	74.46 303	81.13 266	85.37 299	69.79 95	84.42 294	87.95 265	65.03 346	67.46 386	85.33 321	53.28 292	91.73 262	58.01 358	83.27 257	81.85 431
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
tt0320		70.49 365	68.03 373	77.89 336	84.78 314	59.12 341	83.55 315	80.44 388	58.13 418	67.43 388	80.41 406	39.26 420	87.54 358	55.12 379	63.18 434	86.99 357

test_0402		72.79 341	70.44 352	79.84 295	88.13 198	65.99 201	85.93 246	84.29 331	65.57 339	67.40 389	85.49 317	46.92 360	92.61 221	35.88 457	74.38 377	80.94 436

GG-mvs-BLEND				75.38 369	81.59 386	55.80 391	79.32 377	69.63 447		67.19 390	73.67 448	43.24 395	88.90 338	50.41 404	84.50 228	81.45 433

tpmvs		71.09 356	69.29 361	76.49 356	82.04 379	56.04 387	78.92 385	81.37 376	64.05 360	67.18 391	78.28 428	49.74 338	89.77 317	49.67 412	72.37 393	83.67 412

tt0320-xc		70.11 369	67.45 386	78.07 333	85.33 300	59.51 339	83.28 321	78.96 406	58.77 412	67.10 392	80.28 408	36.73 432	87.42 359	56.83 371	59.77 444	87.29 346

OurMVSNet-221017-0		74.26 316	72.42 329	79.80 296	83.76 338	59.59 337	85.92 247	86.64 297	66.39 329	66.96 393	87.58 257	39.46 418	91.60 265	65.76 285	69.27 411	88.22 325

baseline2		75.70 299	73.83 312	81.30 259	83.26 350	61.79 309	82.57 333	80.65 382	66.81 318	66.88 394	83.42 366	57.86 248	92.19 243	63.47 300	79.57 301	89.91 269

F-COLMAP		76.38 291	74.33 305	82.50 232	89.28 149	66.95 186	88.41 153	89.03 229	64.05 360	66.83 395	88.61 228	46.78 363	92.89 211	57.48 361	78.55 311	87.67 335

ACMH	67.68 16	75.89 297	73.93 309	81.77 247	88.71 176	66.61 189	88.62 145	89.01 231	69.81 266	66.78 396	86.70 285	41.95 406	91.51 276	55.64 377	78.14 320	87.17 349
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Syy-MVS		68.05 387	67.85 376	68.67 425	84.68 317	40.97 468	78.62 389	73.08 439	66.65 325	66.74 397	79.46 417	52.11 304	82.30 406	32.89 460	76.38 347	82.75 423

myMVS_eth3d		67.02 394	66.29 394	69.21 420	84.68 317	42.58 463	78.62 389	73.08 439	66.65 325	66.74 397	79.46 417	31.53 446	82.30 406	39.43 452	76.38 347	82.75 423

test0.0.03 1		68.00 388	67.69 381	68.90 422	77.55 427	47.43 445	75.70 416	72.95 441	66.66 322	66.56 399	82.29 386	48.06 353	75.87 444	44.97 439	74.51 376	83.41 414

MDTV_nov1_ep13_2view						37.79 471	75.16 420		55.10 434	66.53 400		49.34 343		53.98 386		87.94 330

KD-MVS_2432*1600		66.22 401	63.89 404	73.21 392	75.47 440	53.42 414	70.76 440	84.35 329	64.10 358	66.52 401	78.52 426	34.55 439	84.98 385	50.40 405	50.33 459	81.23 434

miper_refine_blended		66.22 401	63.89 404	73.21 392	75.47 440	53.42 414	70.76 440	84.35 329	64.10 358	66.52 401	78.52 426	34.55 439	84.98 385	50.40 405	50.33 459	81.23 434

ET-MVSNet_ETH3D		78.63 236	76.63 268	84.64 122	86.73 264	69.47 102	85.01 274	84.61 326	69.54 274	66.51 403	86.59 289	50.16 331	91.75 260	76.26 170	84.24 236	92.69 152

EU-MVSNet		68.53 384	67.61 383	71.31 411	78.51 424	47.01 449	84.47 288	84.27 332	42.27 458	66.44 404	84.79 335	40.44 414	83.76 394	58.76 349	68.54 416	83.17 416

EPNet_dtu		75.46 303	74.86 295	77.23 351	82.57 372	54.60 404	86.89 210	83.09 352	71.64 212	66.25 405	85.86 307	55.99 265	88.04 351	54.92 381	86.55 193	89.05 296
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IMVS_0404		77.16 274	76.42 272	79.37 305	87.13 248	63.59 268	77.12 407	89.33 208	70.51 246	66.22 406	89.03 213	50.36 329	82.78 403	72.56 216	85.56 214	91.74 190

Anonymous20231206		68.60 381	67.80 379	71.02 413	80.23 405	50.75 436	78.30 396	80.47 386	56.79 428	66.11 407	82.63 381	46.35 369	78.95 423	43.62 441	75.70 354	83.36 415

SixPastTwentyTwo		73.37 329	71.26 344	79.70 298	85.08 308	57.89 356	85.57 255	83.56 342	71.03 232	65.66 408	85.88 306	42.10 404	92.57 224	59.11 344	63.34 432	88.65 315

MSDG		73.36 331	70.99 346	80.49 281	84.51 322	65.80 207	80.71 357	86.13 308	65.70 337	65.46 409	83.74 357	44.60 385	90.91 298	51.13 402	76.89 334	84.74 399

OpenMVS_ROB	64.09 19	70.56 363	68.19 369	77.65 343	80.26 403	59.41 340	85.01 274	82.96 357	58.76 413	65.43 410	82.33 384	37.63 430	91.23 287	45.34 438	76.03 351	82.32 426

ppachtmachnet_test		70.04 370	67.34 388	78.14 330	79.80 412	61.13 314	79.19 380	80.59 383	59.16 408	65.27 411	79.29 419	46.75 364	87.29 360	49.33 414	66.72 421	86.00 379

ADS-MVSNet2		66.20 403	63.33 407	74.82 376	79.92 408	58.75 343	67.55 452	75.19 429	53.37 439	65.25 412	75.86 441	42.32 401	80.53 418	41.57 447	68.91 413	85.18 391

ADS-MVSNet		64.36 408	62.88 411	68.78 424	79.92 408	47.17 448	67.55 452	71.18 443	53.37 439	65.25 412	75.86 441	42.32 401	73.99 455	41.57 447	68.91 413	85.18 391

testgi		66.67 397	66.53 393	67.08 432	75.62 438	41.69 467	75.93 412	76.50 424	66.11 331	65.20 414	86.59 289	35.72 437	74.71 451	43.71 440	73.38 388	84.84 398

PM-MVS		66.41 399	64.14 402	73.20 394	73.92 445	56.45 379	78.97 384	64.96 461	63.88 364	64.72 415	80.24 409	19.84 465	83.44 399	66.24 278	64.52 430	79.71 442

FE-MVSNET2		72.88 340	71.28 342	77.67 341	78.30 425	57.78 360	84.43 292	88.92 237	69.56 273	64.61 416	81.67 392	46.73 365	88.54 345	59.33 340	67.99 417	86.69 365

JIA-IIPM		66.32 400	62.82 412	76.82 354	77.09 431	61.72 310	65.34 460	75.38 428	58.04 420	64.51 417	62.32 460	42.05 405	86.51 367	51.45 400	69.22 412	82.21 427

ambc				75.24 371	73.16 452	50.51 437	63.05 467	87.47 278		64.28 418	77.81 432	17.80 467	89.73 319	57.88 359	60.64 441	85.49 385

EG-PatchMatch MVS		74.04 320	71.82 334	80.71 276	84.92 311	67.42 168	85.86 249	88.08 259	66.04 333	64.22 419	83.85 353	35.10 438	92.56 225	57.44 362	80.83 286	82.16 429

UWE-MVS-28		65.32 404	64.93 398	66.49 433	78.70 422	38.55 470	77.86 402	64.39 462	62.00 386	64.13 420	83.60 362	41.44 407	76.00 442	31.39 462	80.89 284	84.92 396

dp		66.80 395	65.43 396	70.90 415	79.74 414	48.82 443	75.12 422	74.77 432	59.61 403	64.08 421	77.23 434	42.89 397	80.72 417	48.86 417	66.58 423	83.16 417

KD-MVS_self_test		68.81 379	67.59 384	72.46 402	74.29 443	45.45 452	77.93 400	87.00 288	63.12 368	63.99 422	78.99 424	42.32 401	84.77 388	56.55 374	64.09 431	87.16 351

pmmvs-eth3d		70.50 364	67.83 378	78.52 324	77.37 429	66.18 195	81.82 338	81.51 373	58.90 411	63.90 423	80.42 405	42.69 399	86.28 370	58.56 350	65.30 428	83.11 418

COLMAP_ROB	66.92 17	73.01 337	70.41 353	80.81 274	87.13 248	65.63 211	88.30 160	84.19 334	62.96 372	63.80 424	87.69 255	38.04 428	92.56 225	46.66 428	74.91 372	84.24 404
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FE-MVSNET1		71.98 350	70.01 357	77.91 335	77.16 430	58.13 349	85.61 253	88.78 241	68.62 299	63.35 425	81.28 394	39.62 417	88.61 342	58.02 357	67.67 418	87.00 356

FMVSNet5		69.50 374	67.96 374	74.15 384	82.97 363	55.35 397	80.01 370	82.12 366	62.56 379	63.02 426	81.53 393	36.92 431	81.92 409	48.42 418	74.06 379	85.17 393

test20.03		67.45 390	66.95 391	68.94 421	75.48 439	44.84 458	77.50 403	77.67 413	66.66 322	63.01 427	83.80 355	47.02 359	78.40 425	42.53 446	68.86 415	83.58 413

K. test v3		71.19 354	68.51 366	79.21 309	83.04 359	57.78 360	84.35 296	76.91 422	72.90 194	62.99 428	82.86 377	39.27 419	91.09 294	61.65 321	52.66 455	88.75 311

our_test_3		69.14 377	67.00 390	75.57 364	79.80 412	58.80 342	77.96 399	77.81 412	59.55 404	62.90 429	78.25 429	47.43 355	83.97 393	51.71 397	67.58 420	83.93 409

CHOSEN 280x420		66.51 398	64.71 400	71.90 404	81.45 389	63.52 273	57.98 469	68.95 451	53.57 438	62.59 430	76.70 436	46.22 371	75.29 450	55.25 378	79.68 300	76.88 448

ttmdpeth		59.91 417	57.10 421	68.34 427	67.13 464	46.65 451	74.64 425	67.41 454	48.30 450	62.52 431	85.04 331	20.40 463	75.93 443	42.55 445	45.90 465	82.44 425

Anonymous20240521		68.80 380	67.22 389	73.55 389	74.33 442	54.11 408	83.18 323	85.61 314	58.15 417	61.68 432	80.94 400	30.71 448	81.27 414	57.00 368	73.34 389	85.28 389

USDC		70.33 366	68.37 367	76.21 358	80.60 400	56.23 385	79.19 380	86.49 300	60.89 392	61.29 433	85.47 318	31.78 445	89.47 324	53.37 390	76.21 350	82.94 422

lessismore_v0				78.97 312	81.01 397	57.15 369		65.99 457		61.16 434	82.82 378	39.12 421	91.34 283	59.67 337	46.92 462	88.43 321

UnsupCasMVSNet_eth		67.33 391	65.99 395	71.37 408	73.48 449	51.47 430	75.16 420	85.19 318	65.20 343	60.78 435	80.93 402	42.35 400	77.20 431	57.12 365	53.69 454	85.44 387

FE-MVSNET		67.25 393	65.33 397	73.02 396	75.86 435	52.54 420	80.26 367	80.56 384	63.80 365	60.39 436	79.70 416	41.41 408	84.66 390	43.34 442	62.62 435	81.86 430

dmvs_testset		62.63 412	64.11 403	58.19 443	78.55 423	24.76 481	75.28 418	65.94 458	67.91 309	60.34 437	76.01 440	53.56 288	73.94 456	31.79 461	67.65 419	75.88 450

AllTest		70.96 357	68.09 372	79.58 302	85.15 305	63.62 264	84.58 286	79.83 396	62.31 381	60.32 438	86.73 279	32.02 443	88.96 336	50.28 407	71.57 401	86.15 373

TestCases				79.58 302	85.15 305	63.62 264		79.83 396	62.31 381	60.32 438	86.73 279	32.02 443	88.96 336	50.28 407	71.57 401	86.15 373

Patchmatch-test		64.82 407	63.24 408	69.57 418	79.42 418	49.82 440	63.49 466	69.05 450	51.98 444	59.95 440	80.13 410	50.91 321	70.98 459	40.66 449	73.57 384	87.90 331

MIMVSNet1		68.58 382	66.78 392	73.98 386	80.07 407	51.82 426	80.77 354	84.37 328	64.40 353	59.75 441	82.16 388	36.47 434	83.63 396	42.73 444	70.33 407	86.48 368

test_vis1_rt		60.28 416	58.42 419	65.84 434	67.25 463	55.60 394	70.44 442	60.94 467	44.33 456	59.00 442	66.64 457	24.91 456	68.67 464	62.80 305	69.48 409	73.25 453

LF4IMVS		64.02 409	62.19 413	69.50 419	70.90 458	53.29 417	76.13 410	77.18 420	52.65 441	58.59 443	80.98 399	23.55 460	76.52 436	53.06 392	66.66 422	78.68 444

PVSNet_0	57.27 20	61.67 415	59.27 418	68.85 423	79.61 415	57.44 366	68.01 450	73.44 438	55.93 432	58.54 444	70.41 455	44.58 386	77.55 430	47.01 427	35.91 467	71.55 455

TDRefinement		67.49 389	64.34 401	76.92 353	73.47 450	61.07 317	84.86 278	82.98 356	59.77 402	58.30 445	85.13 327	26.06 453	87.89 353	47.92 425	60.59 442	81.81 432

mvsany_test3		53.99 424	51.45 429	61.61 440	55.51 474	44.74 459	63.52 465	45.41 479	43.69 457	58.11 446	76.45 438	17.99 466	63.76 470	54.77 382	47.59 461	76.34 449

UnsupCasMVSNet_bld		63.70 410	61.53 416	70.21 417	73.69 447	51.39 431	72.82 431	81.89 368	55.63 433	57.81 447	71.80 452	38.67 424	78.61 424	49.26 415	52.21 457	80.63 438

DSMNet-mixed		57.77 420	56.90 422	60.38 441	67.70 462	35.61 472	69.18 446	53.97 473	32.30 471	57.49 448	79.88 413	40.39 415	68.57 465	38.78 453	72.37 393	76.97 447

N_pmnet		52.79 428	53.26 426	51.40 453	78.99 421	7.68 487	69.52 444	3.89 486	51.63 445	57.01 449	74.98 445	40.83 412	65.96 468	37.78 454	64.67 429	80.56 440

new-patchmatchnet		61.73 414	61.73 415	61.70 439	72.74 455	24.50 482	69.16 447	78.03 411	61.40 389	56.72 450	75.53 444	38.42 425	76.48 437	45.95 434	57.67 445	84.13 406

CMPMVS	51.72 21	70.19 368	68.16 370	76.28 357	73.15 453	57.55 364	79.47 375	83.92 336	48.02 451	56.48 451	84.81 334	43.13 396	86.42 369	62.67 309	81.81 276	84.89 397
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		67.30 392	64.81 399	74.76 377	81.92 382	56.68 377	80.29 365	81.49 374	60.33 396	56.27 452	83.22 368	24.77 457	87.66 357	45.52 436	69.47 410	79.95 441

test_f		52.09 429	50.82 430	55.90 447	53.82 477	42.31 466	59.42 468	58.31 471	36.45 466	56.12 453	70.96 454	12.18 472	57.79 473	53.51 389	56.57 448	67.60 458

YYNet1		65.03 405	62.91 410	71.38 407	75.85 436	56.60 378	69.12 448	74.66 435	57.28 426	54.12 454	77.87 431	45.85 375	74.48 452	49.95 410	61.52 439	83.05 419

MDA-MVSNet_test_wron		65.03 405	62.92 409	71.37 408	75.93 433	56.73 374	69.09 449	74.73 433	57.28 426	54.03 455	77.89 430	45.88 374	74.39 453	49.89 411	61.55 438	82.99 421

pmmvs3		57.79 419	54.26 424	68.37 426	64.02 468	56.72 375	75.12 422	65.17 459	40.20 460	52.93 456	69.86 456	20.36 464	75.48 447	45.45 437	55.25 453	72.90 454

MVS-HIRNet		59.14 418	57.67 420	63.57 437	81.65 384	43.50 461	71.73 434	65.06 460	39.59 462	51.43 457	57.73 465	38.34 426	82.58 405	39.53 450	73.95 380	64.62 461

WB-MVS		54.94 422	54.72 423	55.60 449	73.50 448	20.90 483	74.27 428	61.19 466	59.16 408	50.61 458	74.15 446	47.19 358	75.78 445	17.31 474	35.07 468	70.12 456

MVStest1		56.63 421	52.76 427	68.25 428	61.67 470	53.25 418	71.67 435	68.90 452	38.59 463	50.59 459	83.05 372	25.08 455	70.66 460	36.76 456	38.56 466	80.83 437

MDA-MVSNet-bldmvs		66.68 396	63.66 406	75.75 361	79.28 419	60.56 325	73.92 429	78.35 410	64.43 352	50.13 460	79.87 414	44.02 391	83.67 395	46.10 433	56.86 446	83.03 420

dongtai		45.42 436	45.38 437	45.55 455	73.36 451	26.85 479	67.72 451	34.19 481	54.15 437	49.65 461	56.41 468	25.43 454	62.94 471	19.45 472	28.09 472	46.86 471

SSC-MVS		53.88 425	53.59 425	54.75 451	72.87 454	19.59 484	73.84 430	60.53 468	57.58 424	49.18 462	73.45 449	46.34 370	75.47 448	16.20 477	32.28 470	69.20 457

new_pmnet		50.91 431	50.29 431	52.78 452	68.58 461	34.94 474	63.71 464	56.63 472	39.73 461	44.95 463	65.47 458	21.93 462	58.48 472	34.98 458	56.62 447	64.92 460

test_vis3_rt		49.26 433	47.02 435	56.00 446	54.30 475	45.27 456	66.76 456	48.08 476	36.83 465	44.38 464	53.20 469	7.17 480	64.07 469	56.77 372	55.66 449	58.65 465

kuosan		39.70 440	40.40 441	37.58 458	64.52 467	26.98 477	65.62 459	33.02 482	46.12 453	42.79 465	48.99 471	24.10 459	46.56 479	12.16 480	26.30 473	39.20 472

FPMVS		53.68 426	51.64 428	59.81 442	65.08 466	51.03 433	69.48 445	69.58 448	41.46 459	40.67 466	72.32 451	16.46 469	70.00 463	24.24 470	65.42 427	58.40 466

APD_test1		53.31 427	49.93 432	63.42 438	65.68 465	50.13 438	71.59 436	66.90 456	34.43 468	40.58 467	71.56 453	8.65 478	76.27 439	34.64 459	55.36 451	63.86 462

LCM-MVSNet		54.25 423	49.68 433	67.97 430	53.73 478	45.28 455	66.85 455	80.78 380	35.96 467	39.45 468	62.23 461	8.70 477	78.06 428	48.24 422	51.20 458	80.57 439

PMMVS2		40.82 439	38.86 443	46.69 454	53.84 476	16.45 485	48.61 472	49.92 474	37.49 464	31.67 469	60.97 462	8.14 479	56.42 474	28.42 465	30.72 471	67.19 459

ANet_high		50.57 432	46.10 436	63.99 436	48.67 481	39.13 469	70.99 439	80.85 379	61.39 390	31.18 470	57.70 466	17.02 468	73.65 457	31.22 463	15.89 478	79.18 443

testf1		45.72 434	41.96 438	57.00 444	56.90 472	45.32 453	66.14 457	59.26 469	26.19 472	30.89 471	60.96 463	4.14 481	70.64 461	26.39 468	46.73 463	55.04 467

APD_test2		45.72 434	41.96 438	57.00 444	56.90 472	45.32 453	66.14 457	59.26 469	26.19 472	30.89 471	60.96 463	4.14 481	70.64 461	26.39 468	46.73 463	55.04 467

Gipuma		45.18 437	41.86 440	55.16 450	77.03 432	51.52 429	32.50 475	80.52 385	32.46 470	27.12 473	35.02 474	9.52 476	75.50 446	22.31 471	60.21 443	38.45 473
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 438	40.28 442	55.82 448	40.82 483	42.54 465	65.12 461	63.99 463	34.43 468	24.48 474	57.12 467	3.92 483	76.17 441	17.10 475	55.52 450	48.75 469
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				27.40 461	40.17 484	26.90 478		24.59 485	17.44 477	23.95 475	48.61 472	9.77 475	26.48 480	18.06 473	24.47 474	28.83 474

tmp_tt		18.61 446	21.40 449	10.23 463	4.82 486	10.11 486	34.70 474	30.74 484	1.48 480	23.91 476	26.07 477	28.42 451	13.41 482	27.12 466	15.35 479	7.17 477

test_method		31.52 442	29.28 446	38.23 457	27.03 485	6.50 488	20.94 477	62.21 465	4.05 479	22.35 477	52.50 470	13.33 470	47.58 477	27.04 467	34.04 469	60.62 463

MVE	26.22 23	30.37 444	25.89 448	43.81 456	44.55 482	35.46 473	28.87 476	39.07 480	18.20 476	18.58 478	40.18 473	2.68 484	47.37 478	17.07 476	23.78 475	48.60 470
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 441	30.64 444	35.15 459	52.87 479	27.67 476	57.09 470	47.86 477	24.64 474	16.40 479	33.05 475	11.23 474	54.90 475	14.46 478	18.15 476	22.87 475

EMVS		30.81 443	29.65 445	34.27 460	50.96 480	25.95 480	56.58 471	46.80 478	24.01 475	15.53 480	30.68 476	12.47 471	54.43 476	12.81 479	17.05 477	22.43 476

wuyk23d		16.82 447	15.94 450	19.46 462	58.74 471	31.45 475	39.22 473	3.74 487	6.84 478	6.04 481	2.70 481	1.27 485	24.29 481	10.54 481	14.40 480	2.63 478

EGC-MVSNET		52.07 430	47.05 434	67.14 431	83.51 345	60.71 322	80.50 361	67.75 453	0.07 481	0.43 482	75.85 443	24.26 458	81.54 411	28.82 464	62.25 436	59.16 464

testmvs		6.04 450	8.02 453	0.10 465	0.08 487	0.03 490	69.74 443	0.04 488	0.05 482	0.31 483	1.68 482	0.02 487	0.04 483	0.24 482	0.02 481	0.25 480

test123		6.12 449	8.11 452	0.14 464	0.06 488	0.09 489	71.05 438	0.03 489	0.04 483	0.25 484	1.30 483	0.05 486	0.03 484	0.21 483	0.01 482	0.29 479

mmdepth		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

monomultidepth		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

test_blank		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uanet_test		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

DCPMVS		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

cdsmvs_eth3d_5k		19.96 445	26.61 447	0.00 466	0.00 489	0.00 491	0.00 478	89.26 217	0.00 484	0.00 485	88.61 228	61.62 202	0.00 485	0.00 484	0.00 483	0.00 481

pcd_1.5k_mvsjas		5.26 451	7.02 454	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	63.15 174	0.00 485	0.00 484	0.00 483	0.00 481

sosnet-low-res		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

sosnet		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uncertanet		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

Regformer		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

ab-mvs-re		7.23 448	9.64 451	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	86.72 281	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

uanet		0.00 452	0.00 455	0.00 466	0.00 489	0.00 491	0.00 478	0.00 490	0.00 484	0.00 485	0.00 484	0.00 488	0.00 485	0.00 484	0.00 483	0.00 481

TestfortrainingZip							93.28 12

WAC-MVS						42.58 463								39.46 451

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 52

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 52

eth-test2					0.00 489

eth-test					0.00 489

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

save fliter					93.80 44	72.35 44	90.47 74	91.17 143	74.31 152

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 64

GSMVS																88.96 302

sam_mvs1												51.32 317				88.96 302

sam_mvs												50.01 333

MTGPA								92.02 105

test_post1							78.90 386				5.43 480	48.81 352	85.44 382	59.25 342

test_post											5.46 479	50.36 329	84.24 391

patchmatchnet-post											74.00 447	51.12 320	88.60 343

MTMP							92.18 39	32.83 483

gm-plane-assit					81.40 390	53.83 411			62.72 378		80.94 400		92.39 234	63.40 302

test9_res														84.90 64	95.70 30	92.87 145

agg_prior2														82.91 91	95.45 33	92.70 150

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 80

新几何2							86.29 238

旧先验1					91.96 80	65.79 208		86.37 303			93.08 92	69.31 97			92.74 80	88.74 313

无先验							87.48 186	88.98 232	60.00 400				94.12 140	67.28 271		88.97 301

原ACMM2							86.86 212

testdata2													91.01 296	62.37 312

segment_acmp												73.08 43

testdata1							84.14 302		75.71 107

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 228

plane_prior5								92.44 82					95.38 82	78.71 138	86.32 196	91.33 205

plane_prior4											91.00 157

plane_prior2							91.25 60		79.12 28

plane_prior1					89.90 124

plane_prior						68.71 123	90.38 78		77.62 47						86.16 201

n2								0.00 490

nn								0.00 490

door-mid								69.98 446

test11								92.23 92

door								69.44 449

HQP5-MVS						66.98 183

BP-MVS														77.47 153

HQP3-MVS								92.19 99							85.99 205

HQP2-MVS												60.17 231

NP-MVS					89.62 129	68.32 135					90.24 178

ACMMP++_ref															81.95 274

ACMMP++															81.25 279

Test By Simon												64.33 160

High-resolution multi-view benchmark